simplify start_iteration param for predict in Python and some code cl…

…eanup for start_iteration (#3288)

* simplify start_iteration param for predict in Python and some code cleanup for start_iteration

* revert docs changes about the prediction result shape

R-package/R/lgb.Booster.R

@@ -491,11 +491,11 @@ Booster <- R6::R6Class(
                        header = FALSE,
                        reshape = FALSE, ...) {
 
-      # Check if number of iteration is  non existent
+      # Check if number of iteration is non existent
       if (is.null(num_iteration)) {
         num_iteration <- self$best_iter
       }
-      # Check if start iteration is  non existent
+      # Check if start iteration is non existent
       if (is.null(start_iteration)) {
         start_iteration <- 0L
       }

R-package/tests/testthat/test_Predictor.R

@@ -38,9 +38,8 @@ test_that("start_iteration works correctly", {
         , label = train$label
         , num_leaves = 4L
         , learning_rate = 0.6
-        , nrounds = 100L
+        , nrounds = 50L
         , objective = "binary"
-        , save_name = tempfile(fileext = ".model")
         , valids = list("test" = dtest)
         , early_stopping_rounds = 2L
     )
@@ -50,7 +49,7 @@ test_that("start_iteration works correctly", {
     pred2 <- rep(0.0, length(pred1))
     pred_contrib2 <- rep(0.0, length(pred2))
     step <- 11L
-    end_iter <- 99L
+    end_iter <- 49L
     if (bst$best_iter != -1L) {
         end_iter <- bst$best_iter - 1L
     }

python-package/lightgbm/basic.py

@@ -2813,7 +2813,7 @@ def dump_model(self, num_iteration=None, start_iteration=0, importance_type='spl
                                                           default=json_default_with_numpy))
         return ret
 
-    def predict(self, data, start_iteration=None, num_iteration=None,
+    def predict(self, data, start_iteration=0, num_iteration=None,
                 raw_score=False, pred_leaf=False, pred_contrib=False,
                 data_has_header=False, is_reshape=True, **kwargs):
         """Make a prediction.
@@ -2823,14 +2823,14 @@ def predict(self, data, start_iteration=None, num_iteration=None,
         data : string, numpy array, pandas DataFrame, H2O DataTable's Frame or scipy.sparse
             Data source for prediction.
             If string, it represents the path to txt file.
-        start_iteration : int or None, optional (default=None)
+        start_iteration : int, optional (default=0)
             Start index of the iteration to predict.
-            If None or <= 0, starts from the first iteration.
+            If <= 0, starts from the first iteration.
         num_iteration : int or None, optional (default=None)
-            Limit number of iterations in the prediction.
-            If None, if the best iteration exists and start_iteration is None or <= 0, the best iteration is used;
-            otherwise, all iterations from start_iteration are used.
-            If <= 0, all iterations from start_iteration are used (no limits).
+            Total number of iterations used in the prediction.
+            If None, if the best iteration exists and start_iteration <= 0, the best iteration is used;
+            otherwise, all iterations from ``start_iteration`` are used (no limits).
+            If <= 0, all iterations from ``start_iteration`` are used (no limits).
         raw_score : bool, optional (default=False)
             Whether to predict raw scores.
         pred_leaf : bool, optional (default=False)
@@ -2861,10 +2861,8 @@ def predict(self, data, start_iteration=None, num_iteration=None,
             Can be sparse or a list of sparse objects (each element represents predictions for one class) for feature contributions (when ``pred_contrib=True``).
         """
         predictor = self._to_predictor(copy.deepcopy(kwargs))
-        if start_iteration is None or start_iteration < 0:
-            start_iteration = 0
         if num_iteration is None:
-            if start_iteration == 0:
+            if start_iteration <= 0:
                 num_iteration = self.best_iteration
             else:
                 num_iteration = -1

python-package/lightgbm/sklearn.py

@@ -612,7 +612,7 @@ def _get_meta_data(collection, name, i):
         del train_set, valid_sets
         return self
 
-    def predict(self, X, raw_score=False, start_iteration=None, num_iteration=None,
+    def predict(self, X, raw_score=False, start_iteration=0, num_iteration=None,
                 pred_leaf=False, pred_contrib=False, **kwargs):
         """Return the predicted value for each sample.
 
@@ -622,13 +622,14 @@ def predict(self, X, raw_score=False, start_iteration=None, num_iteration=None,
             Input features matrix.
         raw_score : bool, optional (default=False)
             Whether to predict raw scores.
-        start_iteration : int or None, optional (default=None)
+        start_iteration : int, optional (default=0)
             Start index of the iteration to predict.
-            If None or <= 0, starts from the first iteration.
+            If <= 0, starts from the first iteration.
         num_iteration : int or None, optional (default=None)
-            Limit number of iterations in the prediction.
-            If None, if the best iteration exists, it is used; otherwise, all trees are used.
-            If <= 0, all trees are used (no limits).
+            Total number of iterations used in the prediction.
+            If None, if the best iteration exists and start_iteration <= 0, the best iteration is used;
+            otherwise, all iterations from ``start_iteration`` are used (no limits).
+            If <= 0, all iterations from ``start_iteration`` are used (no limits).
         pred_leaf : bool, optional (default=False)
             Whether to predict leaf index.
         pred_contrib : bool, optional (default=False)
@@ -835,7 +836,7 @@ def fit(self, X, y,
 
     fit.__doc__ = LGBMModel.fit.__doc__
 
-    def predict(self, X, raw_score=False, start_iteration=None, num_iteration=None,
+    def predict(self, X, raw_score=False, start_iteration=0, num_iteration=None,
                 pred_leaf=False, pred_contrib=False, **kwargs):
         """Docstring is inherited from the LGBMModel."""
         result = self.predict_proba(X, raw_score, start_iteration, num_iteration,
@@ -848,7 +849,7 @@ def predict(self, X, raw_score=False, start_iteration=None, num_iteration=None,
 
     predict.__doc__ = LGBMModel.predict.__doc__
 
-    def predict_proba(self, X, raw_score=False, start_iteration=None, num_iteration=None,
+    def predict_proba(self, X, raw_score=False, start_iteration=0, num_iteration=None,
                       pred_leaf=False, pred_contrib=False, **kwargs):
         """Return the predicted probability for each class for each sample.
 
@@ -858,13 +859,14 @@ def predict_proba(self, X, raw_score=False, start_iteration=None, num_iteration=
             Input features matrix.
         raw_score : bool, optional (default=False)
             Whether to predict raw scores.
-        start_iteration : int or None, optional (default=None)
+        start_iteration : int, optional (default=0)
             Start index of the iteration to predict.
-            If None or <= 0, starts from the first iteration.
+            If <= 0, starts from the first iteration.
         num_iteration : int or None, optional (default=None)
-            Limit number of iterations in the prediction.
-            If None, if the best iteration exists, it is used; otherwise, all trees are used.
-            If <= 0, all trees are used (no limits).
+            Total number of iterations used in the prediction.
+            If None, if the best iteration exists and start_iteration <= 0, the best iteration is used;
+            otherwise, all iterations from ``start_iteration`` are used (no limits).
+            If <= 0, all iterations from ``start_iteration`` are used (no limits).
         pred_leaf : bool, optional (default=False)
             Whether to predict leaf index.
         pred_contrib : bool, optional (default=False)

src/application/predictor.hpp

@@ -226,7 +226,6 @@ class Predictor {
                           data_size_t, const std::vector<std::string>& lines) {
       std::vector<std::pair<int, double>> oneline_features;
       std::vector<std::string> result_to_write(lines.size());
-      Log::Warning("before predict_fun_ is called");
       OMP_INIT_EX();
       #pragma omp parallel for schedule(static) firstprivate(oneline_features)
       for (data_size_t i = 0; i < static_cast<data_size_t>(lines.size()); ++i) {
@@ -241,7 +240,6 @@ class Predictor {
         result_to_write[i] = str_result;
         OMP_LOOP_EX_END();
       }
-      Log::Warning("after predict_fun_ is called");
       OMP_THROW_EX();
       for (data_size_t i = 0; i < static_cast<data_size_t>(result_to_write.size()); ++i) {
         writer->Write(result_to_write[i].c_str(), result_to_write[i].size());

src/boosting/gbdt_prediction.cpp

@@ -78,7 +78,7 @@ void GBDT::PredictByMap(const std::unordered_map<int, double>& features, double*
 
 void GBDT::PredictLeafIndex(const double* features, double* output) const {
   int start_tree = start_iteration_for_pred_ * num_tree_per_iteration_;
-  int num_trees =  num_iteration_for_pred_ * num_tree_per_iteration_;
+  int num_trees = num_iteration_for_pred_ * num_tree_per_iteration_;
   const auto* models_ptr = models_.data() + start_tree;
   for (int i = 0; i < num_trees; ++i) {
     output[i] = models_ptr[i]->PredictLeafIndex(features);

tests/python_package_test/test_engine.py

@@ -2321,7 +2321,7 @@ def inner_test(X, y, params, early_stopping_rounds):
             X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
             train_data = lgb.Dataset(X_train, label=y_train)
             valid_data = lgb.Dataset(X_test, label=y_test)
-            booster = lgb.train(params, train_data, num_boost_round=100, early_stopping_rounds=early_stopping_rounds, valid_sets=[valid_data])
+            booster = lgb.train(params, train_data, num_boost_round=50, early_stopping_rounds=early_stopping_rounds, valid_sets=[valid_data])
 
             # test that the predict once with all iterations equals summed results with start_iteration and num_iteration
             all_pred = booster.predict(X, raw_score=True)
@@ -2330,17 +2330,15 @@ def inner_test(X, y, params, early_stopping_rounds):
             for step in steps:
                 pred = np.zeros_like(all_pred)
                 pred_contrib = np.zeros_like(all_pred_contrib)
-                for start_iter in range(0, 100, step):
-                    pred += booster.predict(X, num_iteration=step, start_iteration=start_iter, raw_score=True)
-                    pred_contrib += booster.predict(X, num_iteration=step, start_iteration=start_iter, pred_contrib=True)
+                for start_iter in range(0, 50, step):
+                    pred += booster.predict(X, start_iteration=start_iter, num_iteration=step, raw_score=True)
+                    pred_contrib += booster.predict(X, start_iteration=start_iter, num_iteration=step, pred_contrib=True)
                 np.testing.assert_allclose(all_pred, pred)
                 np.testing.assert_allclose(all_pred_contrib, pred_contrib)
             # test the case where start_iteration <= 0, and num_iteration is None
             pred1 = booster.predict(X, start_iteration=-1)
             pred2 = booster.predict(X, num_iteration=booster.best_iteration)
-            pred3 = booster.predict(X, num_iteration=booster.best_iteration, start_iteration=0)
             np.testing.assert_allclose(pred1, pred2)
-            np.testing.assert_allclose(pred1, pred3)
 
             # test the case where start_iteration > 0, and num_iteration <= 0
             pred4 = booster.predict(X, start_iteration=10, num_iteration=-1)
@@ -2351,14 +2349,14 @@ def inner_test(X, y, params, early_stopping_rounds):
 
             # test the case where start_iteration > 0, and num_iteration <= 0, with pred_leaf=True
             pred4 = booster.predict(X, start_iteration=10, num_iteration=-1, pred_leaf=True)
-            pred5 = booster.predict(X, start_iteration=10, num_iteration=90, pred_leaf=True)
+            pred5 = booster.predict(X, start_iteration=10, num_iteration=40, pred_leaf=True)
             pred6 = booster.predict(X, start_iteration=10, num_iteration=0, pred_leaf=True)
             np.testing.assert_allclose(pred4, pred5)
             np.testing.assert_allclose(pred4, pred6)
 
             # test the case where start_iteration > 0, and num_iteration <= 0, with pred_contrib=True
             pred4 = booster.predict(X, start_iteration=10, num_iteration=-1, pred_contrib=True)
-            pred5 = booster.predict(X, start_iteration=10, num_iteration=90, pred_contrib=True)
+            pred5 = booster.predict(X, start_iteration=10, num_iteration=40, pred_contrib=True)
             pred6 = booster.predict(X, start_iteration=10, num_iteration=0, pred_contrib=True)
             np.testing.assert_allclose(pred4, pred5)
             np.testing.assert_allclose(pred4, pred6)
@@ -2373,7 +2371,7 @@ def inner_test(X, y, params, early_stopping_rounds):
         }
         # test both with and without early stopping
         inner_test(X, y, params, early_stopping_rounds=1)
-        inner_test(X, y, params, early_stopping_rounds=10)
+        inner_test(X, y, params, early_stopping_rounds=5)
         inner_test(X, y, params, early_stopping_rounds=None)
 
         # test for multi-class
@@ -2387,7 +2385,7 @@ def inner_test(X, y, params, early_stopping_rounds):
         }
         # test both with and without early stopping
         inner_test(X, y, params, early_stopping_rounds=1)
-        inner_test(X, y, params, early_stopping_rounds=10)
+        inner_test(X, y, params, early_stopping_rounds=5)
         inner_test(X, y, params, early_stopping_rounds=None)
 
         # test for binary
@@ -2400,5 +2398,5 @@ def inner_test(X, y, params, early_stopping_rounds):
         }
         # test both with and without early stopping
         inner_test(X, y, params, early_stopping_rounds=1)
-        inner_test(X, y, params, early_stopping_rounds=10)
+        inner_test(X, y, params, early_stopping_rounds=5)
         inner_test(X, y, params, early_stopping_rounds=None)