[docs] fixed minor typos in documentation (#1959)

* fixed minor typos in documentation

* fixed typo in gpu_tree_learner.cpp

* Update .gitignore

.gitignore

@@ -390,3 +390,6 @@ R-package/src/src/
 lightgbm_r/*
 lightgbm*.tar.gz
 lightgbm.Rcheck/
+
+# Files generated by aspell
+**/*.bak

README.md

@@ -130,4 +130,4 @@ Huan Zhang, Si Si and Cho-Jui Hsieh. "[GPU Acceleration for Large-scale Tree Boo
 License
 -------
 
-This project is licensed under the terms of the MIT license. See [LICENSE](https://github.com/Microsoft/LightGBM/blob/master/LICENSE) for addtional details.
+This project is licensed under the terms of the MIT license. See [LICENSE](https://github.com/Microsoft/LightGBM/blob/master/LICENSE) for additional details.

docs/Development-Guide.rst

@@ -73,7 +73,7 @@ Refer to `docs README <./README.rst>`__.
 C API
 -----
 
-Refere to the comments in `c\_api.h <https://github.com/Microsoft/LightGBM/blob/master/include/LightGBM/c_api.h>`__.
+Refer to the comments in `c\_api.h <https://github.com/Microsoft/LightGBM/blob/master/include/LightGBM/c_api.h>`__.
 
 High Level Language Package
 ---------------------------

docs/Experiments.rst

@@ -86,7 +86,7 @@ We set up total 3 settings for experiments. The parameters of these settings are
 
 xgboost grows trees depth-wise and controls model complexity by ``max_depth``.
 LightGBM uses a leaf-wise algorithm instead and controls model complexity by ``num_leaves``.
-So we cannot compare them in the exact same model setting. For the tradeoff, we use xgboost with ``max_depth=8``, which will have max number leaves to 255, to compare with LightGBM with ``num_leves=255``.
+So we cannot compare them in the exact same model setting. For the tradeoff, we use xgboost with ``max_depth=8``, which will have max number leaves to 255, to compare with LightGBM with ``num_leaves=255``.
 
 Other parameters are default values.
 

docs/GPU-Targets.rst

@@ -5,7 +5,7 @@ GPU Targets Table
 =================
 
 OpenCL is a universal massively parallel programming framework that targets to multiple backends (GPU, CPU, FPGA, etc).
-Bascially, to use a device from a vendor, you have to install drivers from that specific vendor.
+Basically, to use a device from a vendor, you have to install drivers from that specific vendor.
 Intel's and AMD's OpenCL runtime also include x86 CPU target support.
 NVIDIA's OpenCL runtime only supports NVIDIA GPU (no CPU support).
 In general, OpenCL CPU backends are quite slow, and should be used for testing and debugging only.
@@ -32,7 +32,7 @@ Legend:
 Query OpenCL Devices in Your System
 ===================================
 
-Your system might have multiple GPUs from different vendors ("platforms") installed. Setting up LightGBM GPU device requries two parameters: `OpenCL Platform ID <./Parameters.rst#gpu_platform_id>`__ (``gpu_platform_id``) and `OpenCL Device ID <./Parameters.rst#gpu_device_id>`__ (``gpu_device_id``). Generally speaking, each vendor provides an OpenCL platform, and devices from the same vendor have different device IDs under that platform. For example, if your system has an Intel integrated GPU and two discrete GPUs from AMD, you will have two OpenCL platforms (with ``gpu_platform_id=0`` and ``gpu_platform_id=1``). If the platform 0 is Intel, it has one device (``gpu_device_id=0``) representing the Intel GPU; if the platform 1 is AMD, it has two devices (``gpu_device_id=0``, ``gpu_device_id=1``) representing the two AMD GPUs. If you have a discrete GPU by AMD/NVIDIA and an integrated GPU by Intel, make sure to select the correct ``gpu_platform_id`` to use the discrete GPU as it usually provides better performance.
+Your system might have multiple GPUs from different vendors ("platforms") installed. Setting up LightGBM GPU device requires two parameters: `OpenCL Platform ID <./Parameters.rst#gpu_platform_id>`__ (``gpu_platform_id``) and `OpenCL Device ID <./Parameters.rst#gpu_device_id>`__ (``gpu_device_id``). Generally speaking, each vendor provides an OpenCL platform, and devices from the same vendor have different device IDs under that platform. For example, if your system has an Intel integrated GPU and two discrete GPUs from AMD, you will have two OpenCL platforms (with ``gpu_platform_id=0`` and ``gpu_platform_id=1``). If the platform 0 is Intel, it has one device (``gpu_device_id=0``) representing the Intel GPU; if the platform 1 is AMD, it has two devices (``gpu_device_id=0``, ``gpu_device_id=1``) representing the two AMD GPUs. If you have a discrete GPU by AMD/NVIDIA and an integrated GPU by Intel, make sure to select the correct ``gpu_platform_id`` to use the discrete GPU as it usually provides better performance.
 
 On Windows, OpenCL devices can be queried using `GPUCapsViewer`_, under the OpenCL tab. Note that the platform and device IDs reported by this utility start from 1. So you should minus the reported IDs by 1.
 
@@ -102,7 +102,7 @@ Example of using GPU (``gpu_platform_id = 0`` and ``gpu_device_id = 0`` in our s
     [LightGBM] [Info] Compiling OpenCL Kernel with 16 bins...
     [LightGBM] [Info] GPU programs have been built
     [LightGBM] [Info] Size of histogram bin entry: 12
-    [LightGBM] [Info] 40 dense feature groups (0.12 MB) transfered to GPU in 0.004211 secs. 76 sparse feature groups.
+    [LightGBM] [Info] 40 dense feature groups (0.12 MB) transferred to GPU in 0.004211 secs. 76 sparse feature groups.
     [LightGBM] [Info] No further splits with positive gain, best gain: -inf
     [LightGBM] [Info] Trained a tree with leaves=16 and max_depth=8
     [1]:    test's rmse:1.10643e-17 
@@ -140,7 +140,7 @@ Example of using CPU (``gpu_platform_id = 0``, ``gpu_device_id = 1``). The GPU d
     [LightGBM] [Info] Compiling OpenCL Kernel with 16 bins...
     [LightGBM] [Info] GPU programs have been built
     [LightGBM] [Info] Size of histogram bin entry: 12
-    [LightGBM] [Info] 40 dense feature groups (0.12 MB) transfered to GPU in 0.004540 secs. 76 sparse feature groups.
+    [LightGBM] [Info] 40 dense feature groups (0.12 MB) transferred to GPU in 0.004540 secs. 76 sparse feature groups.
     [LightGBM] [Info] No further splits with positive gain, best gain: -inf
     [LightGBM] [Info] Trained a tree with leaves=16 and max_depth=8
     [1]:    test's rmse:1.10643e-17 

src/treelearner/gpu_tree_learner.cpp

@@ -538,7 +538,7 @@ void GPUTreeLearner::AllocateGPUMemory() {
   }
   // data transfer time
   std::chrono::duration<double, std::milli> end_time = std::chrono::steady_clock::now() - start_time;
-  Log::Info("%d dense feature groups (%.2f MB) transfered to GPU in %f secs. %d sparse feature groups", 
+  Log::Info("%d dense feature groups (%.2f MB) transferred to GPU in %f secs. %d sparse feature groups", 
             dense_feature_group_map_.size(), ((dense_feature_group_map_.size() + (dword_features_ - 1)) / dword_features_) * num_data_ * sizeof(Feature4) / (1024.0 * 1024.0), 
             end_time * 1e-3, sparse_feature_group_map_.size());
   #if GPU_DEBUG >= 1