Sha256: 32842d96ecf5d4addb8992e82eb8046304b2dc970fecca3c3b8828cde0bcdc2e
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Versions: 18
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Stored size: 1.57 KB
Contents
module LightGBM class Classifier < Model def initialize(num_leaves: 31, learning_rate: 0.1, n_estimators: 100, objective: nil, **options) super end def fit(x, y, eval_set: nil, eval_names: [], categorical_feature: "auto", early_stopping_rounds: nil, verbose: true) n_classes = y.uniq.size params = @params.dup if n_classes > 2 params[:objective] ||= "multiclass" params[:num_class] = n_classes else params[:objective] ||= "binary" end train_set = Dataset.new(x, label: y, categorical_feature: categorical_feature, params: params) valid_sets = Array(eval_set).map { |v| Dataset.new(v[0], label: v[1], reference: train_set, params: params) } @booster = LightGBM.train(params, train_set, num_boost_round: @n_estimators, early_stopping_rounds: early_stopping_rounds, verbose_eval: verbose, valid_sets: valid_sets, valid_names: eval_names ) nil end def predict(data, num_iteration: nil) y_pred = @booster.predict(data, num_iteration: num_iteration) if y_pred.first.is_a?(Array) # multiple classes y_pred.map do |v| v.map.with_index.max_by { |v2, _| v2 }.last end else y_pred.map { |v| v > 0.5 ? 1 : 0 } end end def predict_proba(data, num_iteration: nil) y_pred = @booster.predict(data, num_iteration: num_iteration) if y_pred.first.is_a?(Array) # multiple classes y_pred else y_pred.map { |v| [1 - v, v] } end end end end
Version data entries
18 entries across 18 versions & 1 rubygems