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ANN Forecast

Warning

This library is under development, none of the presented solutions are available for download.

Use continuous forest inventory databases to predict forest growth and production. Utilize artificial neural networks for greater flexibility. With this module, you will be able to estimate volume, the number of stems, basal area, among other variables of interest.

Class Parameters

ANN Trainer

AnnTrainer(df, y, *train_columns, iterator=None)
Parameters Description
df The dataframe containing the continous processed forest inventory data.
y The target variable for training the ANN (Y), the variable on which the ANN will be trained to predict.
*train_columns (*args) Names of the columns that will be used to train the artificial neural network so that it can predict the values of Y. Must be numeric.
iterator (Optional) Name of the column that contains the iterator. An artificial neural network will be adjusted for each iterator.

Class Functions

functions and parameters
  AnnTrainer.fit_model(save_dir=None)#(1)!

  1. save_dir = Directory where the .pkl ann file will be saved.
Parameters Description
.fit_model() Adjust the model using *train_columns to predict the variable Y.

Ann structures

6 different structures of artificial neural networks will be tested. Only the result from 1 model will be returned. The model returned will be selected by the ranking function.
For the 'ann' model, the module sklearn.neural_network.MLPRegressor is used.

--- title: ANN Parameters --- classDiagram direction LR class MLPRegressor { Epochs: 3000 Activation: logistic Solver Mode: lbfgs Batch size: dynamic Learning rate init: 0.1 Learning rate mode: adaptive } class Model-0 { Hidden layer sizes: (15, 25, 20, 30, 10) } class Model-1 { Hidden layer sizes: (35, 10, 25, 35, 15) } class Model-2 { Hidden layer sizes: (25, 15, 30, 20) } class Model-3 { Hidden layer sizes: (15, 35, 45) } class Model-4 { Hidden layer sizes: (35, 10, 25, 35, 15) } class Model-5 { Hidden layer sizes: (35, 10, 25, 35, 15, 20, 15, 30) } MLPRegressor <|-- Model-0 MLPRegressor <|-- Model-1 MLPRegressor <|-- Model-2 MLPRegressor <|-- Model-3 MLPRegressor <|-- Model-4 MLPRegressor <|-- Model-5

ANN Predictor

AnnPredictor(pkl_file)
Parameters Description
pkl_file Directory of the .pkl file that will be used for prediction.
Class Functions

functions and parameters
  AnnPredictor.predict(df, *args)#(1)!

  1. Returns the prediction of Y for the *args columns. The *args columns must be the same as those used in *train_columns for training.

Example Usage

ann_forecast_example.py
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from fptools.forecast import AnnTrainer, AnnPredictor#(1)!
import pandas as pd#(2)!

  1. Import AnnTrainer and AnnPredictor class.
  2. Import pandas for data manipulation.

ann_forecast_example.py
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df_train = pd.read_csv(r'C:\Your\path\continuous_inventory_data.csv')#(1)!

df_predictions = pd.read_csv(r'C:\Your\path\new_inventory_data.csv')#(2)!

columns_used_for_training = [
                            "age",
                            "shafts",
                            "basal-area",
                            "HMAX",
                            "DMAX",
                            "DG"
                            ]#(3)!

ann_train = AnnTrainer(df_train, y="commercial-volume",
                       *columns_used_for_training,
                        iterator="Genetic Material")#(4)!

ann_train_metrics = ann_train.fit_model(
                                        save_dir = r"C:\Your\path\output")#(5)!

ann_predictor = AnnPredictor(r"C:\Your\path\output\comercial-volume_ann_predictor_GM-A.pkl")#(6)!

predicted_commercial_volume = ann_predictor.predict(df_predictions, *columns_used_for_training)#(7)!

  1. Load your CSV file for training de ann.
  2. Load your CSV file for predictions.
  3. Create a list containing the names of the columns that will be used to predict Y.
  4. Create the variable ann_train containing the AnnTrainer class using the column comercial-volume as the y, the columns_used_for_training as x and the column Genetic Material as iterator.
  5. Adjust the ann models, saving the .pkl files in the folder C:\Your\path\output, and save the training metrics in the variable ann_train_metrics.
  6. Load the adjusted model for Genetic Material A and save it in the variable ann_predictor.
  7. Make predictions of commercial-volume using the columns columns_used_for_training from df_predictions.