aiq.profiler.forecasting.models.linear_model#

Attributes#

logger

Classes#

LinearModel

A linear regression model that conforms to the BaseModel interface.

Module Contents#

logger#
class LinearModel#

Bases: aiq.profiler.forecasting.models.forecasting_base_model.ForecastingBaseModel

A linear regression model that conforms to the BaseModel interface.

model#
matrix_length = None#
fit(
raw_stats: list[list[aiq.data_models.intermediate_step.IntermediateStep]],
)#

X: shape (N, M) # M = matrix_length * 4 y: shape (N, 4)

predict(
raw_stats: list[list[aiq.data_models.intermediate_step.IntermediateStep]],
) → numpy.ndarray#

Predict using the fitted linear model. Returns shape (N, 4)

_prep_single(raw_stats) → numpy.ndarray#
_prep_for_model_training(raw_stats)#
_extract_token_usage_meta(all_requests_data)#
_preprocess_for_forecasting(arr: numpy.ndarray, matrix_length: int)#

Given a 2D NumPy array arr of shape (n_rows, 4), generate a list of (input_array, output_array) pairs for forecasting, each of shape:

  • input_array: (matrix_length, 4) after padding/trimming

  • output_array: (1, 4)

_flatten_features(x_list, y_list)#

x_list: list of arrays, each of shape (matrix_length, 4) y_list: list of arrays, each of shape (1, 4)

Returns:

x_flat: np.array of shape (N, matrix_length*4) y_flat: np.array of shape (N, 4)