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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,118 @@ | ||
| module LightGBM | ||
| class InnerPredictor | ||
| def initialize(booster, pred_parameter) | ||
| @booster = booster | ||
| @pred_parameter = params_str(pred_parameter) | ||
| end | ||
|
|
||
| def self.from_booster(booster, pred_parameter) | ||
| new(booster, pred_parameter) | ||
| end | ||
|
|
||
| def predict(data, start_iteration: 0, num_iteration: -1, raw_score: false, pred_leaf: false, pred_contrib: false) | ||
| if data.is_a?(Dataset) | ||
| raise TypeError, "Cannot use Dataset instance for prediction, please use raw data instead" | ||
| end | ||
|
|
||
| predict_type = FFI::C_API_PREDICT_NORMAL | ||
| if raw_score | ||
| predict_type = FFI::C_API_PREDICT_RAW_SCORE | ||
| end | ||
| if pred_leaf | ||
| predict_type = FFI::C_API_PREDICT_LEAF_INDEX | ||
| end | ||
| if pred_contrib | ||
| predict_type = FFI::C_API_PREDICT_CONTRIB | ||
| end | ||
|
|
||
| preds, nrow, singular = | ||
| preds_for_data( | ||
| data, | ||
| start_iteration, | ||
| num_iteration, | ||
| predict_type | ||
| ) | ||
|
|
||
| if pred_leaf | ||
| preds = preds.map(&:to_i) | ||
| end | ||
|
|
||
| if preds.size != nrow | ||
| if preds.size % nrow == 0 | ||
| preds = preds.each_slice(preds.size / nrow).to_a | ||
| else | ||
| raise Error, "Length of predict result (#{preds.size}) cannot be divide nrow (#{nrow})" | ||
| end | ||
| end | ||
|
|
||
| singular ? preds.first : preds | ||
| end | ||
|
|
||
| private | ||
|
|
||
| def handle_pointer | ||
| @booster.send(:handle_pointer) | ||
| end | ||
|
|
||
| def preds_for_data(input, start_iteration, num_iteration, predict_type) | ||
| input = | ||
| if daru?(input) | ||
| input[*cached_feature_name].map_rows(&:to_a) | ||
| elsif input.is_a?(Hash) # sort feature.values to match the order of model.feature_name | ||
| sorted_feature_values(input) | ||
| elsif input.is_a?(Array) && input.first.is_a?(Hash) # on multiple elems, if 1st is hash, assume they all are | ||
| input.map(&method(:sorted_feature_values)) | ||
| elsif rover?(input) | ||
| # TODO improve performance | ||
| input[cached_feature_name].to_numo.to_a | ||
| else | ||
| input.to_a | ||
| end | ||
|
|
||
| singular = !input.first.is_a?(Array) | ||
| input = [input] if singular | ||
|
|
||
| nrow = input.count | ||
| n_preds = | ||
| num_preds( | ||
| start_iteration, | ||
| num_iteration, | ||
| nrow, | ||
| predict_type | ||
| ) | ||
|
|
||
| flat_input = input.flatten | ||
| handle_missing(flat_input) | ||
| data = ::FFI::MemoryPointer.new(:double, input.count * input.first.count) | ||
| data.write_array_of_double(flat_input) | ||
|
|
||
| out_len = ::FFI::MemoryPointer.new(:int64) | ||
| out_result = ::FFI::MemoryPointer.new(:double, n_preds) | ||
| check_result FFI.LGBM_BoosterPredictForMat(handle_pointer, data, 1, input.count, input.first.count, 1, predict_type, start_iteration, num_iteration, @pred_parameter, out_len, out_result) | ||
|
|
||
| if n_preds != out_len.read_int64 | ||
| raise Error, "Wrong length for predict results" | ||
| end | ||
|
|
||
| preds = out_result.read_array_of_double(out_len.read_int64) | ||
|
|
||
| [preds, nrow, singular] | ||
| end | ||
|
|
||
| def num_preds(start_iteration, num_iteration, nrow, predict_type) | ||
| out = ::FFI::MemoryPointer.new(:int64) | ||
| check_result FFI.LGBM_BoosterCalcNumPredict(handle_pointer, nrow, predict_type, start_iteration, num_iteration, out) | ||
| out.read_int64 | ||
| end | ||
|
|
||
| def sorted_feature_values(input_hash) | ||
| input_hash.transform_keys(&:to_s).fetch_values(*cached_feature_name) | ||
| end | ||
|
|
||
| def cached_feature_name | ||
| @booster.send(:cached_feature_name) | ||
| end | ||
|
|
||
| include Utils | ||
| end | ||
| end |