train_evaluate

Description

training the models on the training set and predict the target variable values for the training and validation set.

Usage

predict.train_evaluate(training_data, validation_data, model, model_type, model_parameters=None, labels=None, base_models=None, verbose=0)

Parameters

#	Input Name	Input Description
1	training_data	type: dataframe or string default: - details: a data frame or address of a data frame containing data which is used to train the models. This data frame must have a column name format conforming to Fig. 5. example: ‘my_directory/my_data.csv’
2	validation_data	type: dataframe or string default: - details: a data frame or address of a data frame, containing the data that is held out to evaluate models’ performance. This data frame must have a column name format conforming to Fig. 5. example: ‘my_directory/my_data.csv’
3	model	type: {‘knn’, ‘gbm’, ‘glm’, ‘nn’} or callable default: ‘knn’ details: a model to be trained using training data and predict the target variable values. ‘knn’: K nearest neighbour regressor for model_type of regression, K nearest neighbour classifier for model_type of classification. ‘gbm’: Gradient boosting regression for model_type of regression, Gradient boosting classification for model_type of classification. ‘glm’: Elastic net linear regression for model_type of regression, logistic regression for model_type of classification. ‘nn’: Neural network with adjustable number of layers and neurons for model_type of regression and classification. ‘sgcrf’: Sparce gaussian conditional random field (See sgcrfpy) for model_type of regression and classification. [callable]: a user-defined function that accepts the data frames of features in the training and validation set and a target variable values of the training set in a form of ndarray, and returns the predictions on the training and validation set and the trained model (See User defined model). example: ‘nn’ or lstm()
4	model_type	type: {‘regression’,’classification} default: ‘regression’ details: type of prediction task.
5	model_parameters	type: dict or None default: None details: For the predefined models the model hyper parameters can be passed to the function using this argument and in a format of a dictionary. Since for ‘knn’, ‘glm’, and ‘gbm’ we use the implementation of the Scikit Learn package, all of the hyperparameters defined in this package are supported. For the ‘nn’ model the implementation of TensorFlow is used, and the list of supported hyperparameters is as below: [‘hidden_layers_structure’, ‘hidden_layers_number’, ‘hidden_layers_neurons’, ‘hidden_layers_activations’, ‘output_activation’, ‘loss’, ‘optimizer’, ‘metrics’, ‘early_stopping_monitor’, ‘early_stopping_patience’, ‘batch_size’, ‘validation_split’,’epochs’] The structure of the neural network can be specified using ‘hidden_layers_structure’ which takes a list of two item tuples each corresponds to a hidden layer in a network with the first item of each tuple as the number of neurons in the layer and second item as the activation function of the layer. Another way is to specify ‘hidden_layers_number’, ‘hidden_layers_neurons’, and ‘hidden_layers_activations’ which will result in network with the number of hidden layers according to ‘hidden_layers_number’ having the same activation function and number of neurons according to the values of ‘hidden_layers_neurons’ and ‘hidden_layers_activations’ parameters. The ‘output_activation’ hyper parameter is the activation function of the output layer. The supported values for the rest of the hyperparameters are the same as the TensorFlow package. example: {‘n_neighbors’:10, ‘metric’:’minkowski’} {‘hidden_layers_neurons’:[2,4,8], ‘hidden_layers_activations’:[‘relu’,None,’exponential’]}
6	labels	type: list<any type> or None default: None details: The class labels of the target variable.
7	base_models	type: list<string, dict, or function> or None default: None details: a list of predefined model names and user-defined model functions which will be considered as base models to form a mixed model by taking their predictions as the inputs of the main model. If None is passed, the model uses the input data features as the predictors. The supported options are the same as the model input. If the user prefers to specify the hyperparameters of the predefined model, the related item in the list must be in a form of a dictionary with the name of the model as the key and the dictionary of hyperparameters as its value. The specified hyper parameters for each model must conform to the description of model_parameters input. example: [{‘knn’:{‘n_neighbors’:10, ‘metric’:’minkowski’}}, ‘nn’, lstm()]
8	verbose	type: int default: 0 details: The level of details in produced logging information available options 0: no logging 1: only important information logging 2: all details logging

Returns

#	Output Name	Output Description
1	train_predictions	type: list<float> or list<int> details: predictions on training_data which is of type float if model_type is regression, and int if model_type is classification
2	validation_predictions	type: list<float> or list<int> details: predictions on validation_data which is of type float if model_type is regression, and int if model_type is classification
3	trained_model	type: object details: the trained model on the training_data

Example

import pandas as pd
from stpredict.predict import train_evaluate

df = pd.read_csv('./historical_data h=1.csv')
training_df = df.iloc[:-200]
validation_df = df.iloc[-200:]

train_predictions, validation_predictions, trained_model = train_evaluate(
                   training_data = training_df, validation_data = validation_df, model = 'nn',
                   model_parameters = {'hidden_layers_structure':[(2,None),(4,'relu'),(8,'relu')]})