delete links to examples

cases/utils.py

@@ -1,29 +1,18 @@
 import numpy as np
 import pandas as pd
-from fedot.core.data.data import InputData
 from fedot.core.pipelines.pipeline_builder import PipelineBuilder
 from fedot.core.pipelines.tuning.tuner_builder import TunerBuilder
-from fedot.core.repository.dataset_types import DataTypesEnum
 from fedot.core.repository.metrics_repository import RegressionMetricsEnum
-from fedot.core.repository.tasks import TaskTypesEnum, Task
 from golem.core.tuning.simultaneous import SimultaneousTuner
 from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error, median_absolute_error, \
     explained_variance_score, max_error, d2_absolute_error_score
-from sklearn.preprocessing import LabelEncoder
 
 from fedot_ind.api.main import FedotIndustrial
 from fedot_ind.api.utils.path_lib import PROJECT_PATH
 from fedot_ind.core.optimizer.IndustrialEvoOptimizer import IndustrialEvoOptimizer
 from fedot_ind.core.repository.initializer_industrial_models import IndustrialModels
 
 
-def check_multivariate_data(data: pd.DataFrame) -> bool:
-    if isinstance(data.iloc[0, 0], pd.Series):
-        return True
-    else:
-        return False
-
-
 def calculate_regression_metric(test_target, labels):
     test_target = test_target.astype(np.float)
     metric_dict = {'r2_score:': r2_score(test_target, labels),
@@ -40,40 +29,6 @@ def calculate_regression_metric(test_target, labels):
     return df
 
 
-def init_input_data(X: pd.DataFrame, y: np.ndarray, task: str = 'classification') -> InputData:
-    is_multivariate_data = check_multivariate_data(X)
-    task_dict = {'classification': Task(TaskTypesEnum.classification),
-                 'regression': Task(TaskTypesEnum.regression)}
-    features = X.values
-
-    if type((y)[0]) is np.str_ and task == 'classification':
-        label_encoder = LabelEncoder()
-        y = label_encoder.fit_transform(y)
-    elif type((y)[0]) is np.str_ and task == 'regression':
-        y = y.astype(float)
-
-    if is_multivariate_data:
-        input_data = InputData(idx=np.arange(len(X)),
-                               features=np.array(features.tolist()).astype(np.float),
-                               target=y.reshape(-1, 1),
-                               task=task_dict[task],
-                               data_type=DataTypesEnum.image)
-    else:
-        input_data = InputData(idx=np.arange(len(X)),
-                               features=X.values,
-                               target=np.ravel(y).reshape(-1, 1),
-                               task=task_dict[task],
-                               data_type=DataTypesEnum.table)
-
-    if task == 'regression':
-        input_data.target = input_data.target.squeeze()
-    elif task == 'classification':
-        input_data.target[input_data.target == -1] = 0
-    input_data.features = np.where(np.isnan(input_data.features), 0, input_data.features)
-    input_data.features = np.where(np.isinf(input_data.features), 0, input_data.features)
-    return input_data
-
-
 def evaluate_industrial_model(train_data, test_data, task: str = 'regression'):
     metric_dict = {}
     input_data = init_input_data(train_data[0], train_data[1], task=task)

examples/pipeline_example/time_series/neural_networks/advanced_example.py

@@ -1,7 +1,8 @@
 import pandas as pd
 from fedot.core.pipelines.pipeline_builder import PipelineBuilder
 
-from examples.example_utils import evaluate_metric, init_input_data
+from examples.example_utils import evaluate_metric
+from fedot_ind.api.utils.data import init_input_data
 from fedot_ind.core.repository.initializer_industrial_models import IndustrialModels
 from fedot_ind.tools.loader import DataLoader
 

examples/pipeline_example/time_series/neural_networks/basic_example.py

@@ -2,7 +2,8 @@
 from fedot.core.pipelines.node import PipelineNode
 from fedot.core.pipelines.pipeline_builder import PipelineBuilder
 
-from examples.example_utils import evaluate_metric, init_input_data
+from examples.example_utils import evaluate_metric
+from fedot_ind.api.utils.data import init_input_data
 from fedot_ind.core.repository.initializer_industrial_models import IndustrialModels
 from fedot_ind.tools.loader import DataLoader
 from fedot.core.pipelines.pipeline import Pipeline

examples/pipeline_example/time_series/ts_classification/advanced_example.py

@@ -1,7 +1,7 @@
 
 from fedot.core.pipelines.pipeline_builder import PipelineBuilder
 from examples.example_utils import evaluate_metric
-from examples.example_utils import init_input_data
+from fedot_ind.api.utils.data import init_input_data
 from fedot_ind.tools.loader import DataLoader
 from fedot_ind.core.repository.initializer_industrial_models import IndustrialModels
 

examples/pipeline_example/time_series/ts_regression/basic_example.py

@@ -1,6 +1,7 @@
 from fedot.core.pipelines.pipeline_builder import PipelineBuilder
 
-from examples.example_utils import init_input_data, calculate_regression_metric
+from examples.example_utils import calculate_regression_metric
+from fedot_ind.api.utils.data import init_input_data
 from fedot_ind.api.utils.path_lib import PROJECT_PATH
 from fedot_ind.core.repository.initializer_industrial_models import IndustrialModels
 from fedot_ind.tools.loader import DataLoader

fedot_ind/api/utils/checkers_collections.py

@@ -1,12 +1,12 @@
 import logging
 
+from fedot_ind.api.utils.data import check_multivariate_data
 from fedot_ind.core.architecture.settings.computational import backend_methods as np
 from fedot.core.data.data import InputData
 from fedot.core.repository.dataset_types import DataTypesEnum
 from sklearn.preprocessing import LabelEncoder
 from fedot.core.repository.tasks import Task, TaskTypesEnum
 
-from examples.example_utils import check_multivariate_data
 from fedot_ind.core.architecture.preprocessing.data_convertor import NumpyConverter
 
 

fedot_ind/api/utils/data.py

@@ -0,0 +1,47 @@
+import numpy as np
+import pandas as pd
+from fedot.core.data.data import InputData
+from fedot.core.repository.dataset_types import DataTypesEnum
+from fedot.core.repository.tasks import TaskTypesEnum, Task
+from sklearn.preprocessing import LabelEncoder
+
+
+def check_multivariate_data(data: pd.DataFrame) -> bool:
+    if isinstance(data.iloc[0, 0], pd.Series):
+        return True
+    else:
+        return False
+
+
+def init_input_data(X: pd.DataFrame, y: np.ndarray, task: str = 'classification') -> InputData:
+    is_multivariate_data = check_multivariate_data(X)
+    task_dict = {'classification': Task(TaskTypesEnum.classification),
+                 'regression': Task(TaskTypesEnum.regression)}
+    features = X.values
+
+    if type((y)[0]) is np.str_ and task == 'classification':
+        label_encoder = LabelEncoder()
+        y = label_encoder.fit_transform(y)
+    elif type((y)[0]) is np.str_ and task == 'regression':
+        y = y.astype(float)
+
+    if is_multivariate_data:
+        input_data = InputData(idx=np.arange(len(X)),
+                               features=np.array(features.tolist()).astype(np.float),
+                               target=y.reshape(-1, 1),
+                               task=task_dict[task],
+                               data_type=DataTypesEnum.image)
+    else:
+        input_data = InputData(idx=np.arange(len(X)),
+                               features=X.values,
+                               target=np.ravel(y).reshape(-1, 1),
+                               task=task_dict[task],
+                               data_type=DataTypesEnum.table)
+
+    if task == 'regression':
+        input_data.target = input_data.target.squeeze()
+    elif task == 'classification':
+        input_data.target[input_data.target == -1] = 0
+    input_data.features = np.where(np.isnan(input_data.features), 0, input_data.features)
+    input_data.features = np.where(np.isinf(input_data.features), 0, input_data.features)
+    return input_data

fedot_ind/core/architecture/preprocessing/data_convertor.py

@@ -8,7 +8,7 @@
 from pymonad.list import ListMonad
 from sklearn.preprocessing import LabelEncoder
 
-from examples.example_utils import check_multivariate_data
+from fedot_ind.api.utils.data import check_multivariate_data
 from fedot_ind.core.architecture.settings.computational import backend_methods as np
 from fedot_ind.core.architecture.settings.computational import default_device
 from fedot_ind.core.repository.constanst_repository import MATRIX, MULTI_ARRAY

fedot_ind/core/models/base_extractor.py

@@ -10,7 +10,6 @@
 from joblib import delayed, Parallel
 from tqdm import tqdm
 
-from examples.example_utils import init_input_data
 from fedot_ind.core.architecture.abstraction.decorators import convert_to_input_data, fedot_data_type, remove_1_dim_axis
 from fedot_ind.core.metrics.metrics_implementation import *
 from fedot_ind.core.operation.IndustrialCachableOperation import IndustrialCachableOperationImplementation

tests/unit/core/operation/transformation/basis/test_fourier_basis.py

@@ -1,8 +1,8 @@
+from fedot_ind.api.utils.data import init_input_data
 from fedot_ind.core.architecture.settings.computational import backend_methods as np
 import pytest
 from fedot.core.data.data import OutputData
 
-from examples.example_utils import init_input_data
 from fedot_ind.core.operation.transformation.basis.fourier import FourierBasisImplementation
 from fedot_ind.tools.synthetic.ts_datasets_generator import TimeSeriesDatasetsGenerator
 

tests/unit/core/operation/transformation/basis/test_wavelet_basis.py

@@ -1,9 +1,9 @@
+from fedot_ind.api.utils.data import init_input_data
 from fedot_ind.core.architecture.settings.computational import backend_methods as np
 import pytest
 import pywt
 from fedot.core.data.data import OutputData
 
-from examples.example_utils import init_input_data
 from fedot_ind.core.operation.transformation.basis.wavelet import WaveletBasisImplementation
 from fedot_ind.tools.synthetic.ts_datasets_generator import TimeSeriesDatasetsGenerator