x_train.csv (1000 samples, 5 features):
temp_diff;wind_dir;wind_speed;sunshine;humidity
12.5;180;5.2;0.8;65
8.3;90;3.1;0.2;78
...

y_train.csv (1000 samples, 1 output):
energy_consumption
245.6
189.2
...

Classification #

Inputs: Same as Regression
Outputs: Must be one-hot encoded

One-hot encoding: Each output row contains exactly one 1 (indicating the class) and 0 elsewhere.

Example for 3-class classification:

y_train.csv (one-hot encoded):
class_A;class_B;class_C
1;0;0
0;1;0
0;0;1
1;0;0
...

Warning

NeurEco expects one-hot encoded outputs for classification. If your data has integer labels (0, 1, 2, …), you must convert them to one-hot format before training.

Compression #

Inputs: The data you want to compress
Outputs: Not required (the target is to reconstruct the input)

You can provide multiple input files if your data is organized in chunks.

Loading Data #

Loading Training Data

In the Settings tab, click on Training data
Browse and select your input file(s)
If using Regression or Classification, also select your output file(s)

Loading Validation Data (Optional)

Either let NeurEco automatically split your training data (using Validation percentage)
Or provide separate validation files by clicking Validation data

Loading Test Data (Optional)

Click Test data to add files for final evaluation

Loading from NumPy arrays:

import numpy as np
from NeurEco import NeurEcoTabular as Tabular

# Load your data
x_train = np.genfromtxt("x_train.csv", delimiter=";", skip_header=True)
y_train = np.genfromtxt("y_train.csv", delimiter=";", skip_header=True)

# Ensure correct shape for outputs
y_train = np.reshape(y_train, (-1, 1))  # If single output column

# For Regression
config = Tabular.RegressorBuildConfig(
    input_data=x_train,
    output_data=y_train,
    # ... other parameters
)

# For Classification
config = Tabular.ClassifierBuildConfig(
    input_data=x_train,
    output_data=y_train,  # Must be one-hot encoded
    # ... other parameters
)

# For Compression (no output_data needed)
config = Tabular.CompressorBuildConfig(
    input_data=x_train,
    # ... other parameters
)

Loading from files directly:

# You can also pass file paths instead of arrays
config = Tabular.RegressorBuildConfig(
    input_data="path/to/x_train.csv",
    output_data="path/to/y_train.csv",
    # ... other parameters
)

In your configuration file, specify the data paths:

{
    "neurecoRegression_build": {
        "exc_filenames": [
            "x_train.csv"
        ],
        "output_filenames": [
            "y_train.csv"
        ],
        "test_exc_filenames": [
            "x_test.csv"
        ],
        "test_output_filenames": [
            "y_test.csv"
        ]
    }
}

For Compression, use the root key neurecoCompression_build and omit the output file fields.

Data Normalization #

NeurEco automatically normalizes your data before training. Understanding normalization options helps you get better results.

How Normalization Works #

NeurEco applies the formula:

\[x_{normalized} = \frac{x - shift}{scale}\]

Where shift and scale are computed based on your chosen methods.

Shift Methods #

Method	Description
`mean`	Shift by the mean value (centers data around 0)
`min_centered`	Shift so the minimum value becomes -1
`auto`	Automatically chooses: `mean` if data mean is far from 0, otherwise `none`
`none`	No shifting applied

Scale Methods #

Method	Description
`max`	Scale by the maximum absolute value
`max_centered`	Scale so the range becomes [-1, 1]
`std`	Scale by the standard deviation
`auto`	Automatically chooses based on data characteristics
`none`	No scaling applied

Per-Feature Normalization #

The normalize per feature option controls whether normalization is applied independently to each feature or globally across all features.

Set to True when:

Features represent different physical quantities (e.g., temperature and pressure)
You want each feature to have equal importance

Set to False when:

Features represent the same quantity (e.g., temperatures at different locations)
You’re working with field data

In the Settings tab, under Normalization:

Select shift and scale methods from dropdowns
Check/uncheck Normalize per feature for inputs and outputs

config = Tabular.RegressorBuildConfig(
    input_data=x_train,
    output_data=y_train,
    # Input normalization
    inputs_shifting="min_centered",
    inputs_scaling="max_centered",
    inputs_normalize_per_feature=True,
    # Output normalization
    outputs_shifting="auto",
    outputs_scaling="auto",
    outputs_normalize_per_feature=False,
)

{
    "neurecoRegression_build": {
        "input_normalization": {
            "shift_type": "min_centered",
            "scale_type": "max_centered",
            "normalize_per_feature": true
        },
        "output_normalization": {
            "shift_type": "auto",
            "scale_type": "auto",
            "normalize_per_feature": false
        }
    }
}

Tip

If the default normalization doesn’t suit your needs, you can normalize your data yourself before passing it to NeurEco and set normalization to none.

Validation and Test Data #

Training, Validation, and Test Sets #

Training data: Used to train the model
Validation data: Used during training to detect overfitting and stop enrichment
Test data: Used after training to evaluate final model performance

Automatic Splitting #

If you don’t provide separate validation data, NeurEco automatically splits your training data:

Default: 33.33% for validation, 66.67% for training
Adjust via the Validation percentage parameter

In Settings, adjust the Validation percentage slider or enter a value.

config = Tabular.RegressorBuildConfig(
    input_data=x_train,
    output_data=y_train,
    valid_percentage=25.0,  # 25% for validation
)

{
    "neurecoRegression_build": {
        "DevSettings": {
            "valid_percentage": 25.0
        }
    }
}

Providing Separate Validation Data #

For more control, provide your own validation set:

Click Validation data and select your validation files.

config = Tabular.RegressorBuildConfig(
    input_data=x_train,
    output_data=y_train,
    validation_input_data=x_valid,
    validation_output_data=y_valid,
)

{
    "neurecoRegression_build": {
        "exc_filenames": ["x_train.csv"],
        "output_filenames": ["y_train.csv"],
        "validation_exc_filenames": ["x_valid.csv"],
        "validation_output_filenames": ["y_valid.csv"]
    }
}

Best Practices #

Check data quality: Remove duplicates, handle missing values, and identify outliers before training
Ensure consistent shapes: Input and output files must have the same number of samples
Use appropriate normalization: Match normalization settings to your data characteristics (see Best Practices)
Choose validation data carefully: The validation set should represent real-world usage scenarios
Reserve test data: Keep a portion of data completely separate for final evaluation

Next Steps #

Once your data is prepared, proceed to building models.

NeurEco User Manual

Data Preparation

Contents

Data Preparation#

Supported File Formats #

Data Structure #

Input Data #

Output Data #

Template-Specific Requirements #

Regression #

Classification #

Compression #

Loading Data #

Data Normalization #

How Normalization Works #

Shift Methods #

Scale Methods #

Per-Feature Normalization #

Validation and Test Data #

Training, Validation, and Test Sets #

Automatic Splitting #

Providing Separate Validation Data #

Best Practices #

Next Steps #