Прямая визуализация во время обучения

if "google.colab" in str(get_ipython()):
    # Удаление предустановленных пакетов из Colab для избежания конфликтов
    !pip uninstall -y torch notebook notebook_shim tensorflow tensorflow-datasets prophet torchaudio torchdata torchtext torchvision
    !pip install git+https://github.com/ourownstory/neural_prophet.git['live'] # может занять некоторое время
    #!pip install neuralprophet # намного быстрее, но может не иметь последних обновлений/исправлений ошибок

import pandas as pd
from neuralprophet import NeuralProphet

data_location = "https://raw.githubusercontent.com/ourownstory/neuralprophet-data/main/datasets/"
df = pd.read_csv(data_location + "retail_sales.csv")  # Чтение CSV-файла с данными о розничных продажах и загрузка в DataFrame

# Используем метод split_df() из библиотеки NeuralProphet для разделения DataFrame на обучающий и валидационный наборы данных
# valid_p - процент данных, которые будут использоваться для валидации
df_train, df_val = NeuralProphet().split_df(df, valid_p=0.2)

INFO - (NP.df_utils._infer_frequency) - Major frequency MS corresponds to [99.659]% of the data.
INFO - (NP.df_utils._infer_frequency) - Dataframe freq automatically defined as MS
INFO - (NP.df_utils.return_df_in_original_format) - Returning df with no ID column
INFO - (NP.df_utils.return_df_in_original_format) - Returning df with no ID column

# Создаем объект модели NeuralProphet
m = NeuralProphet()

# Обучаем модель на обучающем наборе данных с использованием валидационного набора данных
# Параметр progress устанавливает отображение прогресса обучения в виде графика
metrics = m.fit(df_train, validation_df=df_val, progress="plot")

WARNING - (NP.forecaster.fit) - When Global modeling with local normalization, metrics are displayed in normalized scale.
INFO - (NP.df_utils._infer_frequency) - Major frequency MS corresponds to [99.574]% of the data.
INFO - (NP.df_utils._infer_frequency) - Dataframe freq automatically defined as MS
INFO - (NP.config.init_data_params) - Setting normalization to global as only one dataframe provided for training.
INFO - (NP.utils.set_auto_seasonalities) - Disabling weekly seasonality. Run NeuralProphet with weekly_seasonality=True to override this.
INFO - (NP.utils.set_auto_seasonalities) - Disabling daily seasonality. Run NeuralProphet with daily_seasonality=True to override this.
INFO - (NP.config.set_auto_batch_epoch) - Auto-set batch_size to 16
INFO - (NP.config.set_auto_batch_epoch) - Auto-set epochs to 338
WARNING - (NP.config.set_lr_finder_args) - Learning rate finder: The number of batches (15) is too small than the required number for the learning rate finder (213). The results might not be optimal.

m = NeuralProphet()
metrics = m.fit(df_train, validation_df=df_val, progress="plot-all")

WARNING - (NP.forecaster.fit) - When Global modeling with local normalization, metrics are displayed in normalized scale.
INFO - (NP.df_utils._infer_frequency) - Major frequency MS corresponds to [99.574]% of the data.
INFO - (NP.df_utils._infer_frequency) - Dataframe freq automatically defined as MS
INFO - (NP.config.init_data_params) - Setting normalization to global as only one dataframe provided for training.
INFO - (NP.utils.set_auto_seasonalities) - Disabling weekly seasonality. Run NeuralProphet with weekly_seasonality=True to override this.
INFO - (NP.utils.set_auto_seasonalities) - Disabling daily seasonality. Run NeuralProphet with daily_seasonality=True to override this.
INFO - (NP.config.set_auto_batch_epoch) - Auto-set batch_size to 16
INFO - (NP.config.set_auto_batch_epoch) - Auto-set epochs to 338
WARNING - (NP.config.set_lr_finder_args) - Learning rate finder: The number of batches (15) is too small than the required number for the learning rate finder (213). The results might not be optimal.