modifed rf-python3.6.py

src/python/getting-started/digit-recognizer/rf-python3.6.py

@@ -6,45 +6,45 @@
 Author: 平淡的天
 Github: https://github.com/apachecn/kaggle
 '''
-
+import os.path
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.model_selection import train_test_split
 from sklearn.decomposition import PCA
 import pandas as pd
 # from sklearn.grid_search import GridSearchCV
 # from numpy import arange
 # from lightgbm import LGBMClassifier
+data_dir = \
+    r'/Users/wuyanxue/Documents/GitHub/datasets/getting-started/digit-recognizer/'
 
-train_data = pd.read_csv(r"C:\Users\312\Desktop\digit-recognizer\train.csv")
-test_data = pd.read_csv(r"C:\Users\312\Desktop\digit-recognizer\test.csv")
+train_data = pd.read_csv(os.path.join(data_dir, 'input/train.csv'))
+test_data = pd.read_csv(os.path.join(data_dir, 'input/test.csv'))
 data = pd.concat([train_data, test_data], axis=0).reset_index(drop=True)
 data.drop(['label'], axis=1, inplace=True)
 label = train_data.label
 
 pca = PCA(n_components=100, random_state=34)
 data_pca = pca.fit_transform(data)
 
-Xtrain, Ytrain, xtest, ytest = train_test_split(
+X_train, X_test, y_train, y_test = train_test_split(
     data_pca[0:len(train_data)], label, test_size=0.1, random_state=34)
 
 clf = RandomForestClassifier(
-    n_estimators=110,
-    max_depth=5,
-    min_samples_split=2,
+    n_estimators=100,
+    max_depth=20,
+    min_samples_split=20,
     min_samples_leaf=1,
     random_state=34)
-
 # clf=LGBMClassifier(num_leaves=63, max_depth=7, n_estimators=80, n_jobs=20)
-
 # param_test1 = {'n_estimators':arange(10,150,10),'max_depth':arange(1,11,1)}
 # gsearch1 = GridSearchCV(estimator = clf, param_grid = param_test1, scoring='accuracy',iid=False,cv=5)
 # gsearch1.fit(Xtrain,xtest)
 # print(gsearch1.grid_scores_, gsearch1.best_params_, gsearch1.best_score_)
 
-clf.fit(Xtrain, xtest)
-y_predict = clf.predict(Ytrain)
+clf.fit(X_train, y_train)
+y_predict = clf.predict(X_test)
 
-zeroLable = ytest - y_predict
+zeroLable = y_test - y_predict
 rightCount = 0
 for i in range(len(zeroLable)):
     if list(zeroLable)[i] == 0:
@@ -53,12 +53,8 @@
 
 result = clf.predict(data_pca[len(train_data):])
 
-i = 0
-fw = open("C:\\Users\\312\\Desktop\\digit-recognizer\\result.csv", 'w')
-with open('C:\\Users\\312\\Desktop\\digit-recognizer\\sample_submission.csv'
-          ) as pred_file:
+n, _ = test_data.shape
+with open(os.path.join(data_dir, 'output/Result_sklearn_RF.csv'), 'w') as fw:
     fw.write('{},{}\n'.format('ImageId', 'Label'))
-    for line in pred_file.readlines()[1:]:
-        splits = line.strip().split(',')
-        fw.write('{},{}\n'.format(splits[0], result[i]))
-        i += 1
+    for i in range(1, n + 1):
+        fw.write('{},{}\n'.format(i, result[i - 1]))