Ftrl¶
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class
datatable.models.Ftrl¶ Follow the Regularized Leader (FTRL) model.
FTRL model is a datatable implementation of the FTRL-Proximal online learning algorithm for binomial logistic regression. It uses a hashing trick for feature vectorization and the Hogwild approach for parallelization. FTRL for multinomial classification and continuous targets are implemented experimentally.
See this reference for more details: https://www.eecs.tufts.edu/~dsculley/papers/ad-click-prediction.pdf
Parameters: - alpha (float) – alpha in per-coordinate learning rate algorithm, defaults to 0.005.
- beta (float) – beta in per-coordinate learning rate algorithm, defaults to 1.
- lambda1 (float) – L1 regularization parameter, defaults to 0.
- lambda2 (float) – L2 regularization parameter, defaults to 0.
- nbins (int) – Number of bins to be used for the hashing trick, defaults to 10**6.
- mantissa_nbits (int) – Number of bits from mantissa to be used for hashing, defaults to 10.
- nepochs (int) – Number of training epochs, defaults to 1.
- double_precision (bool) – Whether to use double precision arithmetic or not, defaults to False.
- negative_class (bool) – Whether to create and train on a “negative” class in the case of multinomial classification.
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alpha¶ alpha in per-coordinate FTRL-Proximal algorithm
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beta¶ beta in per-coordinate FTRL-Proximal algorithm
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colname_hashes¶ Column name hashes
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colnames¶ Column names
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double_precision¶ Whether to use double precision arithmetic for modeling
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feature_importances¶ Two-column frame with feature names and the corresponding feature importances normalized to [0; 1].
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fit()¶ Train FTRL model on a dataset.
Parameters: - X_train (Frame) – Training frame of shape (nrows, ncols).
- y_train (Frame) – Target frame of shape (nrows, 1).
- X_validation (Frame) – Validation frame of shape (nrows, ncols).
- y_validation (Frame) – Validation target frame of shape (nrows, 1).
- nepochs_validation (float) – Parameter that specifies how often, in epoch units, validation error should be checked.
- validation_error (float) – If within nepochs_validation relative validation error does not improve by at least validation_error, training stops.
- validation_average_niterations (int) – Number of iterations that is used to calculate average loss. Here, each iteration corresponds to nepochs_validation epochs.
Returns: - A tuple consisting of two elements (epoch and loss, where)
- epoch is the epoch at which model fitting stopped, and loss is the final
- loss. When validation dataset is not provided, epoch returned is equal to
- nepochs, and loss is float(‘nan’).
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interactions¶ List of feature lists to do interactions for
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labels¶ Frame of labels used for classification.
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lambda1¶ L1 regularization parameter
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lambda2¶ L2 regularization parameter
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mantissa_nbits¶ Number of bits from mantissa to be used for hashing float values
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model¶ Model frame of shape (nbins, 2 * nlabels), where nlabels is the total number of labels the model was trained on, and nbins is the number of bins used for the hashing trick. Odd frame columns contain z model coefficients, and even columns n model coefficients.
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model_type¶ ‘auto’, ‘regression’, ‘binomial’ or ‘multinomial.
Type: FTRL model type
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model_type_trained¶ ‘none’, ‘regression’, ‘binomial’ or ‘multinomial.
Type: FTRL trained model type
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nbins¶ Number of bins for the hashing trick
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negative_class¶ Whether to train on negatives in the case of multinomial classification.
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nepochs¶ Number of epochs to train a model
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params¶ FTRL model parameters
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predict()¶ Make predictions for a dataset.
Parameters: X (Frame) – Frame of shape (nrows, ncols) to make predictions for. It should have the same number of columns as the training frame. Returns: - A new frame of shape (nrows, nlabels) with the predicted probabilities
- for each row of frame X and each label the model was trained for.
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reset()¶ Reset FTRL model by clearing all the model weights, labels and feature importance information.
Parameters: None – Returns: Return type: None