[python] migrate to pathlib in python examples (#4428)

examples/python-guide/advanced_example.py

 # coding: utf-8
 import json
 import pickle
+from pathlib import Path
 
 import numpy as np
 import pandas as pd
@@ -10,10 +11,11 @@ import lightgbm as lgb
 
 print('Loading data...')
 # load or create your dataset
-df_train = pd.read_csv('../binary_classification/binary.train', header=None, sep='\t')
-df_test = pd.read_csv('../binary_classification/binary.test', header=None, sep='\t')
-W_train = pd.read_csv('../binary_classification/binary.train.weight', header=None)[0]
-W_test = pd.read_csv('../binary_classification/binary.test.weight', header=None)[0]
+binary_example_dir = Path(__file__).absolute().parents[1] / 'binary_classification'
+df_train = pd.read_csv(str(binary_example_dir / 'binary.train'), header=None, sep='\t')
+df_test = pd.read_csv(str(binary_example_dir / 'binary.test'), header=None, sep='\t')
+W_train = pd.read_csv(str(binary_example_dir / 'binary.train.weight'), header=None)[0]
+W_test = pd.read_csv(str(binary_example_dir / 'binary.test.weight'), header=None)[0]
 
 y_train = df_train[0]
 y_test = df_test[0]

examples/python-guide/dask/ranking.py

-import os
+from pathlib import Path
 
 import dask.array as da
 import numpy as np
@@ -10,10 +10,9 @@ import lightgbm as lgb
 if __name__ == "__main__":
     print("loading data")
 
-    X, y = load_svmlight_file(os.path.join(os.path.dirname(os.path.realpath(__file__)),
-                                           '../../lambdarank/rank.train'))
-    group = np.loadtxt(os.path.join(os.path.dirname(os.path.realpath(__file__)),
-                                    '../../lambdarank/rank.train.query'))
+    rank_example_dir = Path(__file__).absolute().parents[2] / 'lambdarank'
+    X, y = load_svmlight_file(str(rank_example_dir / 'rank.train'))
+    group = np.loadtxt(str(rank_example_dir / 'rank.train.query'))
 
     print("initializing a Dask cluster")
 

examples/python-guide/dataset_from_multi_hdf5.py

+from pathlib import Path
+
 import h5py
 import numpy as np
 import pandas as pd
@@ -97,7 +99,11 @@ def generate_hdf(input_fname, output_basename, batch_size):
 def main():
     batch_size = 64
     output_basename = 'regression'
-    hdf_files = generate_hdf('../regression/regression.train', output_basename, batch_size)
+    hdf_files = generate_hdf(
+        str(Path(__file__).absolute().parents[1] / 'regression' / 'regression.train'),
+        output_basename,
+        batch_size
+    )
 
     create_dataset_from_multiple_hdf(hdf_files, batch_size=batch_size)
 

examples/python-guide/notebooks/interactive_plot_example.ipynb

@@ -18,10 +18,12 @@
    },
    "outputs": [],
    "source": [
-    "import pandas as pd\n",
-    "import lightgbm as lgb\n",
+    "from pathlib import Path\n",
     "\n",
     "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "\n",
+    "import lightgbm as lgb\n",
     "\n",
     "%matplotlib inline\n",
     "\n",
@@ -52,8 +54,9 @@
    },
    "outputs": [],
    "source": [
-    "df_train = pd.read_csv('../../regression/regression.train', header=None, sep='\\t')\n",
-    "df_test = pd.read_csv('../../regression/regression.test', header=None, sep='\\t')\n",
+    "regression_example_dir = Path().absolute().parents[1] / 'regression'\n",
+    "df_train = pd.read_csv(str(regression_example_dir / 'regression.train'), header=None, sep='\\t')\n",
+    "df_test = pd.read_csv(str(regression_example_dir / 'regression.test'), header=None, sep='\\t')\n",
     "\n",
     "y_train = df_train[0]\n",
     "y_test = df_test[0]\n",

examples/python-guide/plot_example.py

 # coding: utf-8
+from pathlib import Path
+
 import pandas as pd
 
 import lightgbm as lgb
@@ -10,8 +12,9 @@ else:
 
 print('Loading data...')
 # load or create your dataset
-df_train = pd.read_csv('../regression/regression.train', header=None, sep='\t')
-df_test = pd.read_csv('../regression/regression.test', header=None, sep='\t')
+regression_example_dir = Path(__file__).absolute().parents[1] / 'regression'
+df_train = pd.read_csv(str(regression_example_dir / 'regression.train'), header=None, sep='\t')
+df_test = pd.read_csv(str(regression_example_dir / 'regression.test'), header=None, sep='\t')
 
 y_train = df_train[0]
 y_test = df_test[0]

examples/python-guide/simple_example.py

 # coding: utf-8
+from pathlib import Path
+
 import pandas as pd
 from sklearn.metrics import mean_squared_error
 
@@ -6,8 +8,9 @@ import lightgbm as lgb
 
 print('Loading data...')
 # load or create your dataset
-df_train = pd.read_csv('../regression/regression.train', header=None, sep='\t')
-df_test = pd.read_csv('../regression/regression.test', header=None, sep='\t')
+regression_example_dir = Path(__file__).absolute().parents[1] / 'regression'
+df_train = pd.read_csv(str(regression_example_dir / 'regression.train'), header=None, sep='\t')
+df_test = pd.read_csv(str(regression_example_dir / 'regression.test'), header=None, sep='\t')
 
 y_train = df_train[0]
 y_test = df_test[0]

examples/python-guide/sklearn_example.py

 # coding: utf-8
+from pathlib import Path
+
 import numpy as np
 import pandas as pd
 from sklearn.metrics import mean_squared_error
@@ -8,8 +10,9 @@ import lightgbm as lgb
 
 print('Loading data...')
 # load or create your dataset
-df_train = pd.read_csv('../regression/regression.train', header=None, sep='\t')
-df_test = pd.read_csv('../regression/regression.test', header=None, sep='\t')
+regression_example_dir = Path(__file__).absolute().parents[1] / 'regression'
+df_train = pd.read_csv(str(regression_example_dir / 'regression.train'), header=None, sep='\t')
+df_test = pd.read_csv(str(regression_example_dir / 'regression.test'), header=None, sep='\t')
 
 y_train = df_train[0]
 y_test = df_test[0]