Python环境安装及数据基本预处理-大数据ML样本集案例实战

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1 Python环境安装

在这里插入图片描述

shift + Enter :换行
ctrl + Enter ：执行

2 Python IDE 环境安装

3 数据预处理

头几行展示

  import numpy as np 
  import pandas as pd 
  import matplotlib.pyplot as plt
  %matplotlib inline
  
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.cross_validation import KFold
  
  # import data
  filename= "C:\\ML\\MLData\\data.csv"
  raw = pd.read_csv(filename)
  print (raw.shape)
  raw.head()

尾几行展示
去除空值

matplot列属性绘制分布

  #plt.subplot(211) first is raw second Column
  # 透明程度 （颜色深度和密度）
  alpha = 0.02
  # 指定图大概占用的区域
  plt.figure(figsize=(10,10))
  # loc_x and loc_y（一行两列第一个位置）
  plt.subplot(121)
  # scatter 散点图
  plt.scatter(kobe.loc_x, kobe.loc_y, color='R', alpha=alpha)
  plt.title('loc_x and loc_y')
  # lat and lon（一行两列第二个位置）
  plt.subplot(122)
  plt.scatter(kobe.lon, kobe.lat, color='B', alpha=alpha)
  plt.title('lat and lon')

角度和极坐标预处理

  raw['dist'] = np.sqrt(raw['loc_x']**2 + raw['loc_y']**2)
  loc_x_zero = raw['loc_x'] == 0
  #print (loc_x_zero)
  raw['angle'] = np.array([0]*len(raw))
  raw['angle'][~loc_x_zero] = np.arctan(raw['loc_y'][~loc_x_zero] / raw['loc_x'][~loc_x_zero])
  raw['angle'][loc_x_zero] = np.pi / 2

时间处理

  raw['remaining_time'] = raw['minutes_remaining'] * 60 + raw['seconds_remaining']

属性唯一值及分组统计打印出来

  投篮方式
  print(kobe.action_type.unique())
  print(kobe.combined_shot_type.unique())
  print(kobe.shot_type.unique())
  分组统计
  print(kobe.shot_type.value_counts())

按列进行特殊符号处理

  kobe['season'].unique()  
  
  array(['2000-01', '2001-02', '2002-03', '2003-04', '2004-05', '2005-06',
         '2006-07', '2007-08', '2008-09', '2009-10', '2010-11', '2011-12',
         '2012-13', '2013-14', '2014-15', '2015-16', '1996-97', '1997-98',
         '1998-99', '1999-00'], dtype=object)

  raw['season'] = raw['season'].apply(lambda x: int(x.split('-')[1]) )
  raw['season'].unique()
  
  array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 97,
        98, 99,  0], dtype=int64)

pd的DataFrame使用技巧（matchup两队对决，opponent对手是谁）
```
  pd.DataFrame({'matchup':kobe.matchup, 'opponent':kobe.opponent})
```

属性相关性展示是否是线性关系（位置和投篮位置）

  plt.figure(figsize=(5,5))
  
  plt.scatter(raw.dist, raw.shot_distance, color='blue')
  plt.title('dist and shot_distance')

pd的groupby对kebe的投射位置进行分组

  gs = kobe.groupby('shot_zone_area')
  print (kobe['shot_zone_area'].value_counts())
  print (len(gs))
  
  Center(C)                11289
  Right Side Center(RC)     3981
  Right Side(R)             3859
  Left Side Center(LC)      3364
  Left Side(L)              3132
  Back Court(BC)              72
  Name: shot_zone_area, dtype: int64
  6

区域划分拉链展示

  import matplotlib.cm as cm
  plt.figure(figsize=(20,10))
  
  def scatter_plot_by_category(feat):
      alpha = 0.1
      gs = kobe.groupby(feat)
      cs = cm.rainbow(np.linspace(0, 1, len(gs)))
      for g, c in zip(gs, cs):
          plt.scatter(g[1].loc_x, g[1].loc_y, color=c, alpha=alpha)
  
  # shot_zone_area
  plt.subplot(131)
  scatter_plot_by_category('shot_zone_area')
  plt.title('shot_zone_area')
  
  # shot_zone_basic
  plt.subplot(132)
  scatter_plot_by_category('shot_zone_basic')
  plt.title('shot_zone_basic')
  
  # shot_zone_range
  plt.subplot(133)
  scatter_plot_by_category('shot_zone_range')
  plt.title('shot_zone_range')

去除某一列

  drops = ['shot_id', 'team_id', 'team_name', 'shot_zone_area', 'shot_zone_range', 'shot_zone_basic', \
           'matchup', 'lon', 'lat', 'seconds_remaining', 'minutes_remaining', \
           'shot_distance', 'loc_x', 'loc_y', 'game_event_id', 'game_id', 'game_date']
  for drop in drops:
      raw = raw.drop(drop, 1)

独热编码（one-hot编码）（一列变多列（0000000）prefix指定添加列前缀）

  print (raw['combined_shot_type'].value_counts())
  pd.get_dummies(raw['combined_shot_type'], prefix='combined_shot_type')[0:2]
  
  Jump Shot    23485
  Layup         5448
  Dunk          1286
  Tip Shot       184
  Hook Shot      153
  Bank Shot      141
  Name: combined_shot_type, dtype: int64

独热编码之后，拼接成1列后，删除对应列。

  categorical_vars = ['action_type', 'combined_shot_type', 'shot_type', 'opponent', 'period', 'season']
  for var in categorical_vars:
      raw = pd.concat([raw, pd.get_dummies(raw[var], prefix=var)], 1)
      raw = raw.drop(var, 1)

4 模型建立

1 测试集和训练集准备

  train_kobe = raw[pd.notnull(raw['shot_made_flag'])]
  train_kobe = train_kobe.drop('shot_made_flag', 1)
  train_label = train_kobe['shot_made_flag']
  test_kobe = raw[pd.isnull(raw['shot_made_flag'])]
  test_kobe = test_kobe.drop('shot_made_flag', 1)

2 随机森林分类

  from sklearn.ensemble import RandomForestRegressor
  from sklearn.metrics import confusion_matrix,log_loss
  import time
  
  # find the best n_estimators for RandomForestClassifier
  print('Finding best n_estimators for RandomForestClassifier...')
  min_score = 100000
  best_n = 0
  scores_n = []
  range_n = np.logspace(0,2,num=3).astype(int)
  for n in range_n:
      print("the number of trees : {0}".format(n))
      t1 = time.time()

  rfc_score = 0.
  rfc = RandomForestClassifier(n_estimators=n)
  for train_k, test_k in KFold(len(train_kobe), n_folds=10, shuffle=True):
  rfc.fit(train_kobe.iloc[train_k], train_label.iloc[train_k])
  #rfc_score += rfc.score(train.iloc[test_k], train_y.iloc[test_k])/10
  pred = rfc.predict(train_kobe.iloc[test_k])
  rfc_score += log_loss(train_label.iloc[test_k], pred) / 10
  scores_n.append(rfc_score)
  if rfc_score < min_score:
  min_score = rfc_score
  best_n = n
  
  t2 = time.time()
  print('Done processing {0} trees ({1:.3f}sec)'.format(n, t2-t1))
  print(best_n, min_score)

  # find best max_depth for RandomForestClassifier
  print('Finding best max_depth for RandomForestClassifier...')
  min_score = 100000
  best_m = 0
  scores_m = []
  range_m = np.logspace(0,2,num=3).astype(int)

for m in range_m:
print(“the max depth : {0}”.format(m))
t1 = time.time()

  rfc_score = 0.
  rfc = RandomForestClassifier(max_depth=m, n_estimators=best_n)
  for train_k, test_k in KFold(len(train_kobe), n_folds=10, shuffle=True):
      rfc.fit(train_kobe.iloc[train_k], train_label.iloc[train_k])
      #rfc_score += rfc.score(train.iloc[test_k], train_y.iloc[test_k])/10
      pred = rfc.predict(train_kobe.iloc[test_k])
      rfc_score += log_loss(train_label.iloc[test_k], pred) / 10
  scores_m.append(rfc_score)
  if rfc_score < min_score:
      min_score = rfc_score
      best_m = m
  
  t2 = time.time()
  print('Done processing {0} trees ({1:.3f}sec)'.format(m, t2-t1))
  print(best_m, min_score)

plt.figure(figsize=(10,5))
plt.subplot(121)
plt.plot(range_n, scores_n)
plt.ylabel('score')
plt.xlabel('number of trees')

plt.subplot(122)
plt.plot(range_m, scores_m)
plt.ylabel('score')
plt.xlabel('max depth')

model = RandomForestClassifier(n_estimators=best_n, max_depth=best_m)
model.fit(train_kobe, train_label)
# 474241623

5 总结

综上所述， numpy与pandas与matplotlit与sklearn四剑客组成了强大的数据分析预处理支持。

秦凯新于深圳 201812081439