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89 changes: 60 additions & 29 deletions
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examples/automl_example/api_example/time_series/ts_forecasting/ts_forecasting_example.py
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import pandas as pd | ||
from fedot.core.pipelines.pipeline_builder import PipelineBuilder | ||
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from fedot_ind.api.main import FedotIndustrial | ||
from fedot_ind.api.utils.path_lib import PROJECT_PATH | ||
from fedot_ind.core.metrics.metrics_implementation import calculate_forecasting_metric | ||
from fedot_ind.core.repository.constanst_repository import M4_FORECASTING_BENCH | ||
from fedot_ind.tools.loader import DataLoader | ||
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if __name__ == "__main__": | ||
dataset_name = 'D1317' | ||
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#dataset_name = 'D1317' | ||
benchmark = 'M4' | ||
horizon = 14 | ||
finetune = False | ||
initial_assumption = PipelineBuilder().add_node('eigen_basis', | ||
params={'low_rank_approximation': False, | ||
'rank_regularization': 'explained_dispersion'}).add_node( | ||
'ar') | ||
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industrial = FedotIndustrial(problem='ts_forecasting', | ||
metric='rmse', | ||
task_params={'forecast_length': horizon}, | ||
timeout=5, | ||
with_tuning=False, | ||
initial_assumption=initial_assumption, | ||
n_jobs=2, | ||
logging_level=10) | ||
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train_data, test_data = DataLoader(dataset_name=dataset_name).load_forecast_data(folder=benchmark) | ||
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if finetune: | ||
model = industrial.finetune(train_data) | ||
else: | ||
model = industrial.fit(train_data) | ||
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labels = industrial.predict(test_data) | ||
probs = industrial.predict_proba(test_data) | ||
metrics = industrial.get_metrics(target=test_data[1], | ||
rounding_order=3, | ||
metric_names=['f1', 'accuracy', 'precision', 'roc_auc']) | ||
print(metrics) | ||
_ = 1 | ||
for dataset_name in M4_FORECASTING_BENCH: | ||
try: | ||
autogluon = PROJECT_PATH + f'/benchmark/results/benchmark_results/autogluon/' \ | ||
f'{dataset_name}_{horizon}_forecast_vs_actual.csv' | ||
n_beats = PROJECT_PATH + f'/benchmark/results/benchmark_results/nbeats/' \ | ||
f'{dataset_name}_{horizon}_forecast_vs_actual.csv' | ||
n_beats = pd.read_csv(n_beats) | ||
autogluon = pd.read_csv(autogluon) | ||
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n_beats_forecast = calculate_forecasting_metric(target=n_beats['value'].values, | ||
labels=n_beats['predict'].values) | ||
autogluon_forecast = calculate_forecasting_metric(target=autogluon['value'].values, | ||
labels=autogluon['predict'].values) | ||
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initial_assumption = PipelineBuilder().add_node('eigen_basis', | ||
params={'low_rank_approximation': False, | ||
'rank_regularization': 'explained_dispersion'}).add_node( | ||
'ar') | ||
industrial = FedotIndustrial(problem='ts_forecasting', | ||
metric='rmse', | ||
task_params={'forecast_length': horizon}, | ||
timeout=5, | ||
with_tuning=False, | ||
initial_assumption=initial_assumption, | ||
n_jobs=2, | ||
logging_level=30) | ||
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train_data, _ = DataLoader(dataset_name=dataset_name).load_forecast_data(folder=benchmark) | ||
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if finetune: | ||
model = industrial.finetune(train_data) | ||
else: | ||
model = industrial.fit(train_data) | ||
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labels = industrial.predict(train_data) | ||
metrics = industrial.get_metrics(target=train_data.values[-horizon:].flatten(), | ||
metric_names=('smape', 'rmse', 'median_absolute_error')) | ||
industrial.save_best_model() | ||
forecast = pd.DataFrame([labels, | ||
train_data.values[-horizon:].flatten(), | ||
autogluon['predict'].values, | ||
n_beats['predict'].values]).T | ||
forecast.columns = ['industrial', 'target', | ||
'AG', | ||
'NBEATS'] | ||
metrics_comprasion = pd.concat([metrics, autogluon_forecast, n_beats_forecast]).T | ||
metrics_comprasion.columns = ['industrial', | ||
'AG', | ||
'NBEATS'] | ||
forecast.to_csv(f'./{dataset_name}_forecast.csv') | ||
metrics_comprasion.to_csv(f'./{dataset_name}_metrics.csv') | ||
except Exception: | ||
print(f'Skip {dataset_name}') | ||
|
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33
examples/real_world_examples/industrial_examples/economic_analysis/requirements.txt
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