mirror of
https://github.com/OpenBB-finance/OpenBB.git
synced 2026-10-28 21:12:16 +00:00
refactor train/valid data
This commit is contained in:
Jamie
@@ -105,10 +105,10 @@ def k_nearest_neighbors(other_args: List[str], s_ticker: str, df_stock: pd.DataF
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parser.add_argument(
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"-t",
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"--test_size",
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default = .2,
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default=0.2,
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dest="test_size",
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type = float,
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help = "Percentage of data to validate in sample"
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type=float,
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help="Percentage of data to validate in sample",
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)
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try:
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@@ -29,6 +29,7 @@ from gamestonk_terminal.prediction_techniques.pred_helper import (
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print_pretty_prediction_nn,
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price_prediction_backtesting_color,
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print_prediction_kpis,
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prepare_scale_train_valid_test,
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)
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from gamestonk_terminal import feature_flags as gtff
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@@ -143,7 +144,7 @@ def _parse_args(prog: str, description: str, other_args: List[str]):
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action="store",
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dest="n_epochs",
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type=check_positive,
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default=200,
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default=50,
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help="number of training epochs.",
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)
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parser.add_argument(
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@@ -251,6 +252,14 @@ def _parse_args(prog: str, description: str, other_args: List[str]):
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default=1,
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help="number of loops to iterate and train models",
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)
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parser.add_argument(
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"-v",
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"--valid",
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type=float,
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dest="valid_split",
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default=0.1,
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help="Validation data split fraction",
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)
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try:
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ns_parser = parse_known_args_and_warn(parser, other_args)
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@@ -1016,59 +1025,80 @@ def lstm(other_args: List[str], s_ticker: str, df_stock: pd.DataFrame):
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return
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# Setup backtesting
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df_stock, df_future = _setup_backtesting(df_stock, ns_parser)
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# df_stock, df_future = _setup_backtesting(df_stock, ns_parser)
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# Pre-process data
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scaler, stock_train_data, stock_x, stock_y = _preprocess_split(
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df_stock, ns_parser
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)
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stock_x = np.reshape(stock_x, (stock_x.shape[0], stock_x.shape[1], 1))
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stock_y = np.reshape(stock_y, (stock_y.shape[0], stock_y.shape[1], 1))
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# scaler, stock_train_data, stock_x, stock_y = _preprocess_split(
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# df_stock, ns_parser
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# )
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# stock_x = np.reshape(stock_x, (stock_x.shape[0], stock_x.shape[1], 1))
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# stock_y = np.reshape(stock_y, (stock_y.shape[0], stock_y.shape[1], 1))
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(
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X_train,
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X_test,
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y_train,
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y_test,
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X_dates_train,
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X_dates_test,
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y_dates_train,
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y_dates_test,
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test_data,
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dates_test,
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scaler,
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) = prepare_scale_train_valid_test(df_stock, ns_parser)
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# Build Neural Network model
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model = build_neural_network_model(
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cfg_nn_models.Long_Short_Term_Memory, ns_parser.n_inputs, ns_parser.n_days
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)
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model.compile(optimizer=ns_parser.s_optimizer, loss=ns_parser.s_loss)
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for idx_loop in range(ns_parser.n_loops):
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# Train our model
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model.fit(
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stock_x,
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stock_y,
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epochs=ns_parser.n_epochs,
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batch_size=ns_parser.n_batch_size,
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verbose=1,
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)
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print("")
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print(model.summary())
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print("")
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# Prediction
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yhat = model.predict(
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stock_train_data[-ns_parser.n_inputs :].reshape(
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1, ns_parser.n_inputs, 1
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),
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verbose=0,
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)
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if idx_loop == 0:
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# Re-scale the data back, plot, and print the results
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df_pred = _rescale_data(
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df_stock, ns_parser, scaler, yhat, idx_loop
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).to_frame()
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else:
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df_pred = df_pred.join(
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_rescale_data(
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df_stock, ns_parser, scaler, yhat, idx_loop
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).to_frame()
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)
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_plot_and_print_results(
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df_stock, ns_parser, df_future, df_pred, "LSTM", s_ticker
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print(X_train.shape, X_test.shape)
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model.fit(
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X_train.reshape(X_train.shape[0], X_train.shape[1], 1),
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y_train,
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epochs=ns_parser.n_epochs,
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verbose=True,
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validation_data=(X_test, y_test),
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)
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# for idx_loop in range(ns_parser.n_loops):
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# Train our model
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# model.fit(
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# stock_x,
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# stock_y,
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# epochs=ns_parser.n_epochs,
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# batch_size=ns_parser.n_batch_size,
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# verbose=1,
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# )
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# print("")
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# print(model.summary())
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# print("")
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# # Prediction
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# yhat = model.predict(
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# stock_train_data[-ns_parser.n_inputs :].reshape(
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# 1, ns_parser.n_inputs, 1
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# ),
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# verbose=0,
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# )
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# if idx_loop == 0:
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# Re-scale the data back, plot, and print the results
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# df_pred = _rescale_data(
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# df_stock, ns_parser, scaler, yhat, idx_loop
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# ).to_frame()
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# else:
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# df_pred = df_pred.join(
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# _rescale_data(
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# df_stock, ns_parser, scaler, yhat, idx_loop
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# ).to_frame()
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# )
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# _plot_and_print_results(
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# df_stock, ns_parser, df_future, df_pred, "LSTM", s_ticker
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# )
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except Exception as e:
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print(e)
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traceback.print_exc()
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@@ -1,8 +1,9 @@
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""" Prediction helper functions """
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__docformat__ = "numpy"
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import argparse
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from typing import Tuple
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from datetime import datetime
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from datetime import datetime, timedelta
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import numpy as np
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import pandas as pd
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from pandas.plotting import register_matplotlib_converters
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@@ -14,7 +15,7 @@ from sklearn.metrics import (
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mean_squared_error,
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)
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from sklearn.model_selection import train_test_split
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from sklearn.preprocessing import StandardScaler, MinMaxScaler, Normalizer
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from gamestonk_terminal.helper_funcs import (
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get_next_stock_market_days,
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patch_pandas_text_adjustment,
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@@ -26,26 +27,72 @@ from gamestonk_terminal.config_plot import PLOT_DPI
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register_matplotlib_converters()
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def prepare_train_valid_test(
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df_stock, parsed_end_date, n_input_days, n_predict_days, test_size
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def prepare_scale_train_valid_test(
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df_stock: pd.DataFrame, ns_parser: argparse.Namespace
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):
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"""
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Prepare and scale train, validate and test data.
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Parameters
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----------
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df_stock: pd.DataFrame
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Dataframe of stock prices
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ns_parser: argparse.Namespace
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Parsed arguments
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Returns
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-------
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X_train: np.ndarray
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Array of training data. Shape (# samples, n_inputs, 1)
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X_test: np.ndarray
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Array of validation data. Shape (#samples, n_inputs, 1)
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y_train: np.ndarray
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Array of training outputs. Shape (#samples, n_days)
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y_test: np.ndarray
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Array of validation outputs. Shape (#samples, n_days)
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X_dates_train: np.ndarray
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Array of dates for X_train
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X_dates_test: np.ndarray
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Array of dates for X_test
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y_dates_train: np.ndarray
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Array of dates for y_train
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y_dates_test: np.ndarray
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Array of dates for y)test
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test_data: np.ndarray
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Array of prices after the specified end date
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dates_test: np.ndarray
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Array of dates after specified end date
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scaler:
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Fitted preprocesser
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"""
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parsed_end_date = ns_parser.s_end_date or df_stock.index[-1]-timedelta(days=3)
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n_input_days = ns_parser.n_inputs
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n_predict_days = ns_parser.n_days
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test_size = ns_parser.valid_split
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df_test = df_stock[df_stock.index >= parsed_end_date]
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df_stock = df_stock[df_stock.index < parsed_end_date]
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dates = df_stock.index
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prices = df_stock["5. adjusted close"].values
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# Pre-process data
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if ns_parser.s_preprocessing == "standardization":
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scaler = StandardScaler()
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elif ns_parser.s_preprocessing == "minmax":
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scaler = MinMaxScaler()
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elif ns_parser.s_preprocessing == "normalization":
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scaler = Normalizer()
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test_data = df_stock[df_stock.index > parsed_end_date]
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train_data = df_stock[df_stock.index < parsed_end_date]
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dates = train_data.index
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dates_test = test_data.index
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scaler = scaler
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train_data = scaler.fit_transform(train_data.values.reshape(-1, 1))
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test_data = scaler.transform(test_data.values.reshape(-1, 1))
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prices = train_data
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input_dates = []
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input_prices = []
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next_n_day_prices = []
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next_n_day_dates = []
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test_days = []
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test_inputs = []
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test_next_n_days = []
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for idx in range(len(prices) - n_input_days - n_predict_days):
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input_prices.append(prices[idx : idx + n_input_days])
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input_dates.append(dates[idx : idx + n_input_days])
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next_n_day_prices.append(
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@@ -55,20 +102,20 @@ def prepare_train_valid_test(
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dates[idx + n_input_days : idx + n_input_days + n_predict_days]
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)
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input_dates = np.asarray(input_dates)
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input_prices = np.asarray(input_prices)
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next_n_day_prices = np.asarray(next_n_day_prices)
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next_n_day_dates = np.asarray(next_n_day_dates)
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input_dates = np.array(input_dates)
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input_prices = np.array(input_prices)
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next_n_day_prices = np.array(next_n_day_prices)
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next_n_day_dates = np.array(next_n_day_dates)
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(
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X_train,
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X_valid,
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X_test,
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y_train,
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y_valid,
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y_test,
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X_dates_train,
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X_dates_valid,
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X_dates_test,
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y_dates_train,
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y_dates_valid,
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y_dates_test,
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) = train_test_split(
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input_prices,
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next_n_day_prices,
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@@ -76,15 +123,19 @@ def prepare_train_valid_test(
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next_n_day_dates,
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test_size=test_size,
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)
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return (
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X_train,
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X_valid,
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X_test,
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y_train,
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y_valid,
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y_test,
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X_dates_train,
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X_dates_valid,
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X_dates_test,
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y_dates_train,
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y_dates_valid,
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y_dates_test,
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test_data,
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dates_test,
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scaler,
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)
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@@ -144,132 +195,6 @@ def get_backtesting_data(
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return df_future, df_stock, True
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def plot_pred(
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df_stock: pd.DataFrame,
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df_future: pd.DataFrame,
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df_pred: pd.DataFrame,
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title: str,
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bt_flag: bool,
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):
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plt.figure(figsize=plot_autoscale(), dpi=PLOT_DPI)
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plt.plot(df_stock.index, df_stock["5. adjusted close"], lw=2)
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plt.title(title)
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plt.xlim(df_stock.index[0], get_next_stock_market_days(df_pred.index[-1], 1)[-1])
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plt.xlabel("Time")
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plt.ylabel("Share Price ($)")
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plt.grid(b=True, which="major", color="#666666", linestyle="-")
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plt.minorticks_on()
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plt.grid(b=True, which="minor", color="#999999", linestyle="-", alpha=0.2)
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df_ma = df_stock["5. adjusted close"].rolling(window=ns_parser.n_length).mean()
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plt.plot(df_ma.index, df_ma, lw=2, linestyle="--", c="tab:orange")
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plt.plot(
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[df_stock.index[-1], df_pred.index[0]],
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[df_stock["5. adjusted close"].values[-1], df_pred.values[0]],
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lw=1,
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c="tab:green",
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linestyle="--",
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)
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plt.plot(df_pred.index, df_pred, lw=2, c="tab:green")
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plt.axvspan(
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df_stock.index[-1], df_pred.index[-1], facecolor="tab:orange", alpha=0.2
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)
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_, _, ymin, ymax = plt.axis()
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plt.vlines(df_stock.index[-1], ymin, ymax, linewidth=1, linestyle="--", color="k")
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if bt_flag:
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plt.figure(figsize=plot_autoscale(), dpi=PLOT_DPI)
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plt.subplot(211)
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plt.plot(
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df_future.index,
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df_future["5. adjusted close"],
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lw=2,
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c="tab:blue",
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ls="--",
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)
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plt.plot(df_pred.index, df_pred, lw=2, c="green")
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plt.scatter(df_future.index, df_future["5. adjusted close"], c="tab:blue", lw=3)
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plt.plot(
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[df_stock.index[-1], df_future.index[0]],
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[
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df_stock["5. adjusted close"].values[-1],
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df_future["5. adjusted close"].values[0],
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],
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lw=2,
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c="tab:blue",
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ls="--",
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)
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plt.scatter(df_pred.index, df_pred, c="green", lw=3)
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plt.plot(
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[df_stock.index[-1], df_pred.index[0]],
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[df_stock["5. adjusted close"].values[-1], df_pred.values[0]],
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lw=2,
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c="green",
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ls="--",
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)
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plt.title("BACKTESTING: Real data price versus Prediction")
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plt.xlim(df_stock.index[-1], df_pred.index[-1] + datetime.timedelta(days=1))
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plt.xticks(
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[df_stock.index[-1], df_pred.index[-1] + datetime.timedelta(days=1)],
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visible=True,
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)
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plt.ylabel("Share Price ($)")
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plt.grid(b=True, which="major", color="#666666", linestyle="-")
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plt.minorticks_on()
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plt.grid(b=True, which="minor", color="#999999", linestyle="-", alpha=0.2)
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plt.legend(["Real data", "Prediction data"])
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plt.xticks([])
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plt.subplot(212)
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plt.axhline(y=0, color="k", linestyle="--", linewidth=2)
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plt.plot(
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df_future.index,
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100
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* (df_pred.values - df_future["5. adjusted close"].values)
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/ df_future["5. adjusted close"].values,
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lw=2,
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c="red",
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)
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plt.scatter(
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df_future.index,
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100
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* (df_pred.values - df_future["5. adjusted close"].values)
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/ df_future["5. adjusted close"].values,
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c="red",
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lw=5,
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)
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plt.title("BACKTESTING: Error between Real data and Prediction [%]")
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plt.plot(
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[df_stock.index[-1], df_future.index[0]],
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[
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0,
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100
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* (df_pred.values[0] - df_future["5. adjusted close"].values[0])
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/ df_future["5. adjusted close"].values[0],
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],
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lw=2,
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ls="--",
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c="red",
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)
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plt.xlim(df_stock.index[-1], df_pred.index[-1] + datetime.timedelta(days=1))
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plt.xticks(
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[df_stock.index[-1], df_pred.index[-1] + datetime.timedelta(days=1)],
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visible=True,
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)
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plt.xlabel("Time")
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plt.ylabel("Prediction Error (%)")
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plt.grid(b=True, which="major", color="#666666", linestyle="-")
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plt.minorticks_on()
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plt.grid(b=True, which="minor", color="#999999", linestyle="-", alpha=0.2)
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plt.legend(["Real data", "Prediction data"])
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if gtff.USE_ION:
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plt.ion()
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||||
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plt.show()
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pass
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def price_prediction_color(val: float, last_val: float) -> str:
|
||||
"""Set prediction to be a colored string"""
|
||||
if float(val) > last_val:
|
||||
|
||||
Reference in New Issue
Block a user