> ## Documentation Index
> Fetch the complete documentation index at: https://aidflow.aigensys.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Parameters

> AID.core.parameters.Parameters

```python theme={"theme":{"light":"dracula-soft","dark":"dracula"}}
from AID.core.parameters import Parameters
```

AIDflow parametrization design.

Under the hood, `ProjectContext.params()` provides kwargs to Parameters for
validation and application of default options.

The created Parameters instance is set to the context scope as `ctx.setup` and
serves as the global default configuration for all workers.

Each worker inherits required parameters from `ctx.setup`, but allows custom overrides
at instantiation: `worker(param1=1, param2=2)`.

#### Parameters

Genesis

**`project`** : *`str`*
Unique identifier for the project.\
Used as a root directory of model artifacts, and logging files to ensure traceability.

**`target`** : *`str`*
The name of the target (label) column in the input dataset. Specifies the dependent variable\
the model will learn to predict.

**`input_data`** : *`str`*
Defines path and format of the input dataset.\
1\. file: file name (if in data) or full path\
2\. spark: connection e.g. catalog.db.table\_name\
3\. db: connection e.g. postgresql://user:pass\@localhost:5432/mydb\
When local files are used:\
• Files in `data` folder can be loaded by their filename only, e.g. *test.csv*.\
• Files located out of the main project dir have to use OS path to the data file.

**`input_source`** : *`str = "file"`*
Defines the ingestion engine. Options:\
• file:  Local or network file system (csv / gz / parquet / feather)\
• spark: Distributed Spark DataFrame\
• db:    SQL Database connection

**`input_cols`** : *`list = None`*
Explicit list of columns to load. Leave empty to load ALL available columns from the source.\
Supports also syntax by dictionary:\
• `{"remove": "COL1", "COL2"}`\
• `{"keep": "COL1", "COL2"}`

**`flow_vars`** : *`list = None`*
Columns to retain as non-feature metadata/IDs. These are excluded from feature engineering but        preserved for model application (scoring) and traceability.

**`yy_mm`** : *`str = None`*
The column name representing the timestamp/decision date. Used for time-based train/test\
splits and PSI stability checks.\
If no time variable is specified, an artificial one is generated based on row order.

**`yy_mm_format`** : *`str = None`*
The strftime format string used to parse the time column. Standard Python % directives apply.\
Common Examples:\
• "2023-01-15" -> "%Y-%m-%d"\
• "15Jan23" -> "%d%b%y"

**`import_start`** : *`float = 0.0`*
Fraction of the dataset to start loading from. Useful if the dataset head contains stale/noisy\
data or if processing in chunks.

**`import_end`** : *`float = 1.0`*
Fraction of the dataset to stop loading at. Allows testing with subsets before committing to\
full data ingestion.

**`train_test`** : *`float = 0.3`*
Proportion of the loaded data reserved for the Test Set (held out). The remaining (1-ratio) is\
used for training/validation.

**`train_valid`** : *`float = 0.33`*
Proportion of the Training data further split into a Validation Set. Used for hyperparameter\
tuning and early stopping.

**`smp_train`** : *`int|float = 1.0`*
Sub-sampling ratio for the Training set to accelerate the learning during prototyping.\
• Value over 1 represents sample observation count.\
• Value between (0, 1) represents fraction of the input dataset.

**`smp_feature`** : *`int|float = 1.0`*
Sub-sampling ratio of input variables to reduce dimensionality and accelerate the learning\
during prototyping.\
• Value over 1 represents sample feature count.\
• Value between (0, 1) represents fraction of the features in the input dataset.

Data

**`max_miss`** : *`float = 0.7`*
Drop variables with missing values exceeding set percentage.

**`max_1val`** : *`float = 0.9`*
Drop variables where > X% of values are identical (mode). High share indicates lack of variance        (e.g., constant columns).

**`max_var`** : *`float = 0.9`*
Threshold to detect quasi-IDs (variables with extremely high cardinality/variability).\
Variables exceeding this are removed to prevent overfitting.

**`max_corr`** : *`float = 0.9`*
Threshold for pairwise correlation. Variables with absolute correlation > X are considered redundant        (multicollinearity) and one is dropped.

**`cat_max`** : *`int = 500`*
Maximum number of unique categories allowed in a categorical variable. Variables exceeding        this are dropped or require special handling.

**`th_stationarity`** : *`float = 0.1`*
p-value threshold for the Dickey-Fuller test. Variables with p-value > X are considered\
non-stationary (unstable over time).

**`th_outliers`** : *`float = 0.01`*
Expected proportion of outliers (noise/errors) in the data. Outliers algorithm uses the set\
value as the data cutoff threshold.

**`float_downcast`** : *`int | bool = 0`*
Downcast float64 variable to float32. When enabled, all 64-bit float variables (NumPy & PyArrow)\
are cast to 32-bit float to reduce memory footprint.

Encoding

**`gini_cutoff`** : *`float = 0.02`*
Minimum Gini Index required for a feature to be retained. Features with lower predictive\
power are dropped.

**`min_bin_share`** : *`float = 0.03`*
Minimum relative size (share) for a bin (cat + num). Bins smaller than this are merged\
with adjacent bins to ensure stability.

**`max_num_bins`** : *`int = 10`*
aximum N bins to explore during ML numerical binning transformation. Binning exploration\
goes from 2 to N.

**`num_alpha`** : *`float = 0.05`*
Significance level (alpha) for statistical tests during binning optimization.\
Different bin versions are considered statistically equal if feature performance Bin1 \~ Bin2.

**`ixn_strategy`** : *`int = 1`*
Strategy for generating interaction features:\
• 1: LightGBM (LGB) based interactions\
• 2: XGBoost (XGB) based interactions

**`ixn_max_leaves`** : *`int = 8`*
Interaction GB-algo critical gen setup of interaction depth.\
Maximum number of leaves per tree. Controls model complexity/depth.

**`ixn_min_target`** : *`float = 0.10`*
Minimum target rate (imbalance metric) required in an interaction feature.\
Interactions below this threshold are pruned as noise.

**`woe_strategy`** : *`int = 1`*
Minimum target rate (imbalance metric) required in an interaction variable.\
Transformation Strategy to WOE\
• 1: Gini search\
• 2: Gini + t-test search

**`woe_alpha`** : *`float = 0.05`*
Significance level for bin pruning and stability checks during WOE transformation.

**`woe_stability`** : *`float = 0.65`*
Predictive Stability Index cutoff. Features with PSI \< X are removed.

Training

**`gb_strategy`** : *`int = 1`*
Model training by GBM Tree Strategy:\
• 1: LightGBM (LGB)\
• 2: XGBoost (XGB)

**`gb_max_leaves`** : *`int = 31`*
Maximum number of leaves per tree. Controls model complexity/depth.

**`gb_estimators`** : *`int = 100`*
Total number of boosting stages (trees) to be added.

**`gb_learning_rate`** : *`float = 0.1`*
Step size shrinkage used in updating leaves. Lower values require more trees.

**`gb_min_child_weight`** : *`float = 0.1`*
Minimum sum of instance weights (Hessian) needed in a child node.\
Prevents overfitting by limiting splits with low data support.

**`shadow_quantile`** : *`float = 0.9`*
During Feature Pruning the shadow features are generated as permutated orig.\
features to provide a noise distribution. Shadow quantile value set to 1 means\
max value from the noise dst (stronger pruning) 0 min (weaker pruning).

**`cross_val_folds`** : *`int = 3`*
Number of cross-validation folds for shadow assessment and where feature significance\
to model residuals is assessed.

**`glm_strategy`** : *`int = 1`*
Strategy for GLM Dense Optimization\
• 1: LassoGLM\
• 2: ElasticGLM\
• 3: LogRegGLM

**`glm_alpha`** : *`float = 0.05`*
Significance level during GLM Sparse training to add/remove *significant*\
feature given change in model residulas p-value.

**`glm_eps`** : *`float = 1e-3`*
Epsilon (eps) represents a relative search range. Defines the ratio between\
the strongest and weakest regularization strength.\
• eps=1e-3 means that alpha\_min / alpha\_max = 1e-3

**`glm_tol`** : *`float = 1e-4`*
Tolerance (tol) represents optimization accuracy. Defines when the change in\
the loss function is small enough to stop computing a single model.

Selection

**`select_alpha`** : *`float = 0.05`*
Significance level during tuning phase for agents comparison and cross behavior checks.

**`select_bins`** : *`float = 10`*
Selection phase transform continous probability to distinct bins for comparison and\
transition matrix.

**`select_choice`** : *`int = 0`*
Selection of the final production candidate:\
• 0: Auto select the best one based on comparison scoring\
• 1: Sparse Selector \<br> 2: Dense Selector\
• 3: PLS Selector\
• 4: Tree Selector

Output

**`save`** : *`bool | int = 1`*
Set to 1 (True) to persist models, metrics, reports and artifacts to the project output folder.

**`logging`** : *`bool | int = 1`*
Set to 1 (True) to persist logging during training to the project output folder "run.log";\
independent of verbosity level and save state.

**`offload`** : *`bool | int = 0`*
Offloads relay-data structures to disk via the Arrow engine to reduce memory footprint.\
Enable when working with large feature spaces or limited RAM. Trade-off: minor I/O\
overhead on access in exchange for significantly lower peak memory usage.

**`verbose`** : *`int = 2`*
Controls the amount of output:\
• 0: Silent (Production mode)\
• 1: Info (Standard logs)\
• 2: GUI Interactive Panels & Reports


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