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Merge branch 'industrial_release_0.5' of https://github.com/aimclub/F…
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…edot.Industrial into industrial_release_0.5
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@ismonnar
ismonnar committed Jan 27, 2025
2 parents 2be42b9 + 6c4eee5 commit e75f57f
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54 changes: 25 additions & 29 deletions examples/automl_example/custom_strategy/specific_strategy/probability_calibration_example.py
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@@ -1,42 +1,38 @@
import numpy as np

from examples.example_utils import create_feature_generator_strategy
from fedot_ind.core.architecture.pipelines.abstract_pipeline import ApiTemplate
from fedot_ind.core.repository.config_repository import DEFAULT_COMPUTE_CONFIG, \
DEFAULT_AUTOML_LEARNING_CONFIG

INDUSTRIAL_PARAMS = {'data_type': 'tensor',
'learning_strategy': 'ts2tabular'
}

# DEFINE ALL CONFIG FOR API
AUTOML_LEARNING_STRATEGY = DEFAULT_AUTOML_LEARNING_CONFIG
COMPUTE_CONFIG = DEFAULT_COMPUTE_CONFIG
AUTOML_CONFIG = {'task': 'classification'}
LEARNING_CONFIG = {'learning_strategy': 'from_scratch',
'learning_strategy_params': AUTOML_LEARNING_STRATEGY,
'optimisation_loss': {'quality_loss': 'f1'}}
INDUSTRIAL_CONFIG = {'problem': 'classification',
'strategy': 'tabular',
'strategy_params': INDUSTRIAL_PARAMS
}
API_CONFIG = {'industrial_config': INDUSTRIAL_CONFIG,
'automl_config': AUTOML_CONFIG,
'learning_config': LEARNING_CONFIG,
'compute_config': COMPUTE_CONFIG}

if __name__ == "__main__":
dataset_name = 'Libras'
finetune = False
api_config = dict(problem='classification',
metric='f1',
timeout=0.1,
n_jobs=2,
logging_level=20)
DATASET_NAME = 'Libras'

feature_generator, sampling_dict = create_feature_generator_strategy()
DEFAULT_AUTOML_LEARNING_CONFIG['timeout'] = 0.1

AUTOML_CONFIG = {'task': 'classification'}
LEARNING_CONFIG = {'learning_strategy': 'from_scratch',
'learning_strategy_params': DEFAULT_AUTOML_LEARNING_CONFIG,
'optimisation_loss': {'quality_loss': 'f1'}}
INDUSTRIAL_CONFIG = {'problem': 'classification',
'strategy': 'tabular',
'strategy_params': {'feature_generator': feature_generator,
'data_type': 'tensor',
'learning_strategy': 'ts2tabular',
'sampling_strategy': sampling_dict}}

API_CONFIG = {'industrial_config': INDUSTRIAL_CONFIG,
'automl_config': AUTOML_CONFIG,
'learning_config': LEARNING_CONFIG,
'compute_config': DEFAULT_COMPUTE_CONFIG}

api_client = ApiTemplate(api_config=API_CONFIG,
metric_list=('f1', 'accuracy'))
result_dict = api_client.eval(dataset=dataset_name, finetune=finetune)
uncalibrated_labels, uncalibrated_probs = result_dict['industrial_model'].predicted_labels, \
result_dict['industrial_model'].predicted_probs
result_dict = api_client.eval(dataset=DATASET_NAME, finetune=False)
uncalibrated_labels, uncalibrated_probs = result_dict['industrial_model'].manager.predicted_labels, \
result_dict['industrial_model'].manager.predicted_probs
calibrated_probs = result_dict['industrial_model'].predict_proba(predict_data=api_client.test_data,
calibrate_probs=True)
calibrated_labels = np.argmax(calibrated_probs, axis=1) + np.min(uncalibrated_labels)
67 changes: 30 additions & 37 deletions examples/automl_example/time_series/ts_forecasting/forecasting_strategy_example.py
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@@ -1,50 +1,43 @@
import pickle

from fedot_ind.core.architecture.pipelines.abstract_pipeline import ApiTemplate
from fedot_ind.core.repository.config_repository import DEFAULT_COMPUTE_CONFIG, DEFAULT_TSF_AUTOML_CONFIG, \
DEFAULT_AUTOML_LEARNING_CONFIG
from fedot_ind.core.repository.constanst_repository import M4_FORECASTING_BENCH

finetune = False


def forecasting_loop(dataset_dict, api_config):
metric_names = ('rmse', 'smape')
result_dict = ApiTemplate(api_config=api_config,
metric_list=metric_names).eval(dataset=dataset_dict,
finetune=finetune)

return result_dict


def evaluate_for_M4(type: str = 'M'):
dataset_list = [data for data in M4_FORECASTING_BENCH if data.__contains__(type)]
return dataset_list


if __name__ == "__main__":
bench = 'M4'
group = 'M'
forecast_params = {'forecast_length': 8}
horizon = forecast_params['forecast_length']
dataset_list = evaluate_for_M4(group)
api_config = dict(
problem='ts_forecasting',
metric='rmse',
timeout=5,
with_tuning=False,
industrial_strategy='forecasting_assumptions',
industrial_strategy_params={
'industrial_task': 'ts_forecasting',
'data_type': 'time_series'},
task_params=forecast_params,
logging_level=50)
METRIC_NAMES = ('rmse', 'smape')
HORIZON = 8
TASK_PARAMS = {'forecast_length': HORIZON}
BENCH = 'M4'
GROUP = 'M'

DATASET_NAMES = [data for data in M4_FORECASTING_BENCH if data.__contains__(GROUP)]

DEFAULT_AUTOML_LEARNING_CONFIG['timeout'] = 5

API_CONFIG = {'industrial_config': {'problem': 'ts_forecasting',
'data_type': 'time_series',
'learning_strategy': 'forecasting_assumptions',
'task_params': TASK_PARAMS},
'automl_config': {**DEFAULT_TSF_AUTOML_CONFIG,
'task_params': TASK_PARAMS},
'learning_config': {'learning_strategy': 'from_scratch',
'learning_strategy_params': DEFAULT_AUTOML_LEARNING_CONFIG,
'optimisation_loss': {'quality_loss': 'rmse'}},
'compute_config': DEFAULT_COMPUTE_CONFIG}

result_dict = {}

for dataset_name in dataset_list:
dataset_dict = {'benchmark': bench,
for dataset_name in DATASET_NAMES:
dataset_dict = {'benchmark': BENCH,
'dataset': dataset_name,
'task_params': forecast_params}
result_dict = forecasting_loop(dataset_dict, api_config)
'task_params': TASK_PARAMS}
result_dict = ApiTemplate(api_config=API_CONFIG,
metric_list=METRIC_NAMES).eval(dataset=dataset_dict,
finetune=False)
result_dict.update({dataset_name: result_dict})

with open(f'{bench}_{group}_forecast_length_{horizon}.pkl', 'wb') as f:
with open(f'{BENCH}_{GROUP}_forecast_length_{HORIZON}.pkl', 'wb') as f:
pickle.dump(result_dict, f)
54 changes: 29 additions & 25 deletions examples/automl_example/time_series/ts_forecasting/ts_forecasting_exogen.py
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@@ -1,35 +1,39 @@
import numpy as np
import pandas as pd

from examples.example_utils import load_monash_dataset
from fedot_ind.api.main import FedotIndustrial
from fedot_ind.tools.serialisation.path_lib import PROJECT_PATH
from fedot_ind.core.repository.config_repository import DEFAULT_COMPUTE_CONFIG, DEFAULT_TSF_AUTOML_CONFIG

if __name__ == "__main__":
dataset_name = PROJECT_PATH + \
'/examples/data/forecasting\\monash_benchmark\\MonashBitcoin_30.csv'
horizon = 60
metric_names = ('smape', 'rmse', 'median_absolute_error')
HORIZON = 60
METRIC_NAMES = ('smape', 'rmse', 'median_absolute_error')

train_data = pd.read_csv(dataset_name)
variables = train_data['label'].unique().tolist()
exog_var = ['send_usd', 'market_cap',
'median_transaction_value', 'google_trends']
exog_ts = np.vstack(
[train_data[train_data['label'] == var]['value'].values for var in exog_var])
train_data = load_monash_dataset('bitcoin')
exog_var = ['send_usd', 'market_cap', 'median_transaction_value', 'google_trends']
exog_ts = np.vstack([train_data[column].values for column in exog_var])
exog_ts = exog_ts[0, :]
ts = train_data[train_data['label'] == 'price']['value'].values
target = ts[-horizon:].flatten()
ts = train_data['price'].values
target = ts[-HORIZON:].flatten()
input_data = (ts, target)

api_config = dict(problem='ts_forecasting',
metric='rmse',
timeout=15,
with_tuning=False,
pop_size=10,
industrial_strategy_params={'exog_variable': exog_ts},
task_params={'forecast_length': horizon},
industrial_strategy='forecasting_exogenous',
n_jobs=2,
logging_level=30)
industrial = FedotIndustrial(**api_config)
TASK_PARAMS = {'forecast_length': HORIZON}
AUTOML_LEARNING_STRATEGY = dict(timeout=3,
with_tuning=False,
n_jobs=2,
pop_size=10,
logging_level=30)

API_CONFIG = {'industrial_config': {'problem': 'ts_forecasting',
'task_params': TASK_PARAMS,
'strategy': 'forecasting_exogenous',
'strategy_params': {'exog_variable': exog_ts,
'data_type': 'time_series'}},
'automl_config': {'task_params': TASK_PARAMS,
**DEFAULT_TSF_AUTOML_CONFIG},
'learning_config': {'learning_strategy': 'from_scratch',
'learning_strategy_params': AUTOML_LEARNING_STRATEGY,
'optimisation_loss': {'quality_loss': 'rmse'}},
'compute_config': DEFAULT_COMPUTE_CONFIG}

industrial = FedotIndustrial(**API_CONFIG)
industrial.fit(input_data)
2 changes: 1 addition & 1 deletion fedot_ind/api/utils/api_init.py
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Expand Up @@ -95,7 +95,7 @@ def build(self, config: dict = None):
self.config.update({key: val})
if self.strategy in FEDOT_INDUSTRIAL_STRATEGY:
self.strategy = IndustrialStrategy(industrial_strategy=self.strategy,
industrial_strategy_params=self.task_params,
industrial_strategy_params=self.strategy_params,
api_config=self.config)
return self

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2 changes: 2 additions & 0 deletions fedot_ind/core/repository/config_repository.py
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Expand Up @@ -32,9 +32,11 @@ class AutomlConfigConstant(Enum):
'optimisation_agent': 'Industrial'}}
DEFAULT_CLF_AUTOML_CONFIG = {'task': 'classification', **DEFAULT_SUBCONFIG}
DEFAULT_REG_AUTOML_CONFIG = {'task': 'regression', **DEFAULT_SUBCONFIG}
DEFAULT_TSF_AUTOML_CONFIG = {'task': 'ts_forecasting', **DEFAULT_SUBCONFIG}


DEFAULT_AUTOML_LEARNING_CONFIG = AutomlLearningConfigConstant.DEFAULT_AUTOML_CONFIG.value
DEFAULT_COMPUTE_CONFIG = ComputeConfigConstant.DEFAULT_COMPUTE_CONFIG.value
DEFAULT_CLF_AUTOML_CONFIG = AutomlConfigConstant.DEFAULT_CLF_AUTOML_CONFIG.value
DEFAULT_REG_AUTOML_CONFIG = AutomlConfigConstant.DEFAULT_REG_AUTOML_CONFIG.value
DEFAULT_TSF_AUTOML_CONFIG = AutomlConfigConstant.DEFAULT_TSF_AUTOML_CONFIG.value

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