mirror of
https://github.com/OpenBB-finance/OpenBB.git
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6426b76771
* decorated functions and removed assertion tests that are now caught by decorator logs * modified functions with @property * fixed bc of coingecko * fixed outedated tests
129 lines
3.3 KiB
Python
129 lines
3.3 KiB
Python
"""Regression Model"""
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__docformat__ = "numpy"
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import logging
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from typing import Any, List, Tuple, Union
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import numpy as np
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import pandas as pd
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from sklearn import linear_model, pipeline, preprocessing
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from gamestonk_terminal.decorators import log_start_end
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from gamestonk_terminal.rich_config import console
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# The tsxv dependence was removed so this fails. Taking from didiers source
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logger = logging.getLogger(__name__)
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@log_start_end(log=logger)
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def split_train(
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sequence: np.ndarray, numInputs: int, numOutputs: int, numJumps: int
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) -> Tuple[List, List]:
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"""Returns sets to train a model
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i.e. X[0] = sequence[0], ..., sequence[numInputs]
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y[0] = sequence[numInputs+1], ..., sequence[numInputs+numOutputs]
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...
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X[k] = sequence[k*numJumps], ..., sequence[k*numJumps+numInputs]
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y[k] = sequence[k*numJumps+numInputs+1], ..., sequence[k*numJumps+numInputs+numOutputs]
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Parameters
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----------
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sequence: np.ndarray
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Full training dataset
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numInputs : int
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Number of inputs X used at each training
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numOutputs : int
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Number of outputs y used at each training
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numJumps : int
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Number of sequence samples to be ignored between (X,y) sets
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Returns
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-------
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List
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List of numInputs arrays
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List
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List of numOutputs arrays
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"""
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X: List = []
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y: List = []
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if numInputs + numOutputs > len(sequence):
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console.print(
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"To have at least one X,y arrays, the sequence size needs to be bigger than numInputs+numOutputs"
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)
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return X, y
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for i in range(len(sequence)):
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i = numJumps * i
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end_ix = i + numInputs
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# Once train data crosses time series length return
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if end_ix + numOutputs > len(sequence):
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break
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seq_x = sequence[i:end_ix]
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X.append(seq_x)
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seq_y = sequence[end_ix : end_ix + numOutputs]
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y.append(seq_y)
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return X, y
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@log_start_end(log=logger)
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def get_regression_model(
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values: Union[pd.Series, pd.DataFrame],
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poly_order: int,
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n_input: int,
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n_predict: int,
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n_jumps: int,
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) -> Tuple[List[float], Any]:
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"""Fit regression model of variable order
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Parameters
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----------
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values : Union[pd.Series, pd.DataFrame]
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Data to fit
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poly_order : int
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Order of polynomial
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n_input : int
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Length of input sequence
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n_predict : int
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Length of prediction sequence
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n_jumps : int
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Number of jumps in data preparation
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Returns
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-------
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List[float]
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List of predicted values
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Any
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Linear model fit to data
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"""
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# Split training data
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stock_x, stock_y = split_train(
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values.values,
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n_input,
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n_predict,
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n_jumps,
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)
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if not stock_x:
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console.print("Given the model parameters more training data is needed.\n")
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return [], None
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# Machine Learning model
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if poly_order == 1:
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model = linear_model.LinearRegression(n_jobs=-1)
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else:
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model = pipeline.make_pipeline(
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preprocessing.PolynomialFeatures(poly_order), linear_model.Ridge()
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)
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model.fit(stock_x, stock_y)
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l_predictions = [
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i if i > 0 else 0
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for i in model.predict(values.values[-n_input:].reshape(1, -1))[0]
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]
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return l_predictions, model
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