Kaggle实战-Rain-in-Australia-预测明天有没有下雨，机器学习，二分类
Rain-in-AustraliaMachine learing by xgboost项目旨在通过机器学习算法寻找一个有效而健壮的天气预测模型。针对训练集及测试集中大量缺省值，项目使用具体城市具体特征median进行填充，从而避免对整体数据产生重大影响。针对数字项异常值，项目采用Winsorizing，将（0.25,0.75）外的值使用最值代替。项目对所给训练集的不同天气特征进行特征工程，以最大限
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Rain-in-Australia
代码位置：GitHub 麻烦给个小心心
Machine learing by xgboost
项目旨在通过机器学习算法寻找一个有效而健壮的天气预测模型。
针对训练集及测试集中大量缺省值，项目使用具体城市具体特征median进行填充，从而避免对整体数据产生重大影响。针对数字项异常值，项目采用Winsorizing，将（0.25,0.75）外的值使用最值代替。项目对所给训练集的不同天气特征进行特征工程，以最大限度地从原始数据中提取特征以供算法和模型使用。地点特征工程是项目难点，而项目采用的是基于opencage API，将地点转为对应浮点型经纬度进行处理。对于模型参数，项目采用gridSearchCV网格搜索，指标设定为机器学习f1值，在优秀的GPU算力下进行长时间针对不同模型的调参工作。其结果表明XGBClassifier是最合适的模型。通过实际数据与预测数据进行比较，XGBClassifier在达到较高准确率时获得更好的召回率，进而实现f1值得提高。
#引入包
import time,json,joblib
import pandas as pd
import numpy as np
from sklearn import metrics
from sklearn.linear_model import LinearRegression,LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVR
from sklearn.preprocessing import StandardScaler
from xgboost import XGBClassifier
from sklearn.metrics import confusion_matrix, accuracy_score, f1_score, roc_auc_score, roc_curve
from sklearn.model_selection import train_test_split,StratifiedKFold, GridSearchCV, cross_val_score
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(style="whitegrid")
path = 'work/home/aistudio/data/data74989/'
#函数 将地点城市名称转为对应的经纬度
from opencage.geocoder import OpenCageGeocode
def getGeCode(pot):
key = 'xxxxxxxxxxxxxxxxxxxxxxx' #可从opencage官网获取
geocoder = OpenCageGeocode(key)
query = pot + ',Australian'
res = geocoder.geocode(query)
if len(res) > 0:
results = dict(res[0])
results = results['annotations']
results = results['DMS']
lat = str(results['lat'])
lng = str(results['lng'])
lat = lat.replace(lat[-1], '').replace(' ', '').replace('°', ' ').replace('\'', ' ').split(' ')
lng = lng.replace(lng[-1], '').replace(' ', '').replace('°', ' ').replace('\'', ' ').split(' ')
lat = (float(lat[0]) * 3600 + float(lat[1]) * 60 + float(lat[2]))
lng = (float(lng[0]) * 3600 + float(lng[1]) * 60 + float(lng[2]))
else:
lat = None
lng = None
return ({
"lat": lat,
"lng": lng
})
导入数据集
# 导入数据集
train = pd.read_csv(path+'train.csv')
test = pd.read_csv(path+'test.csv')
len(train)
116368
查看一共多少个城市监测站
# 将不同的Location转为经纬度
list_Location = train['Location'].unique()
dic_Location = {}
for city in list_Location:
dic_Location[city] = getGeCode(city)
dic_Location
# 储存为json数据
with open(path+'city.json','w') as file_obj:
json.dump(dic_Location,file_obj)
# 从json数据读取
with open(path+'city.json','r') as file_obj:
dic_Location = json.load(file_obj)
dic_Location
{'Dartmoor': {'lat': 136489.6692, 'lng': 508280.13288},
'Newcastle': {'lat': 135811.2276, 'lng': 521636.06376},
'Albany': {'lat': 129916.49652, 'lng': 528874.47792},
'Ballarat': {'lat': 135224.83836, 'lng': 517908.20796},
'Uluru': {'lat': 135811.2276, 'lng': 521636.06376},
'Bendigo': {'lat': 132282.31824, 'lng': 519328.575},
'Cairns': {'lat': 119746.71576, 'lng': 416631.02688},
'Townsville': {'lat': 69340.1058, 'lng': 528553.49112},
'GoldCoast': {'lat': 100930.30272, 'lng': 552265.83936},
'Sale': {'lat': 135160.70112, 'lng': 517657.9842},
'PerthAirport': {'lat': 114960.18996, 'lng': 417483.31464},
'NorahHead': {'lat': 135811.2276, 'lng': 521636.06376},
'Watsonia': {'lat': 135656.98344, 'lng': 522255.68532},
'Woomera': {'lat': 135811.2276, 'lng': 521636.06376},
'NorfolkIsland': {'lat': 135811.2276, 'lng': 521636.06376},
'Richmond': {'lat': 74630.54592, 'lng': 515315.13912},
'Brisbane': {'lat': 98912.33316, 'lng': 550873.269},
'Wollongong': {'lat': 123936.37956, 'lng': 543204.67248},
'SydneyAirport': {'lat': 135811.2276, 'lng': 521636.06376},
'Melbourne': {'lat': 136156.97952, 'lng': 521791.3098},
'MelbourneAirport': {'lat': 135636.20388, 'lng': 521463.12156},
'Nhil': {'lat': 135811.2276, 'lng': 521636.06376},
'Sydney': {'lat': 121947.55164, 'lng': 544368.48012},
'MountGinini': {'lat': 135811.2276, 'lng': 521636.06376},
'Adelaide': {'lat': 125672.391, 'lng': 498935.76984},
'MountGambier': {'lat': 136144.63764, 'lng': 506899.19592},
'Mildura': {'lat': 127154.37096, 'lng': 536934.78},
'Portland': {'lat': 137951.20116, 'lng': 509748.57288},
'PearceRAAF': {'lat': 135811.2276, 'lng': 521636.06376},
'Moree': {'lat': 106451.91, 'lng': 534761.98164},
'Witchcliffe': {'lat': 122278.89672, 'lng': 414126.42732},
'Walpole': {'lat': 135811.2276, 'lng': 521636.06376},
'Cobar': {'lat': 119828.71908, 'lng': 416656.17684},
'AliceSprings': {'lat': 135811.2276, 'lng': 521636.06376},
'Albury': {'lat': 129916.49652, 'lng': 528874.47792},
'Darwin': {'lat': 135811.2276, 'lng': 521636.06376},
'BadgerysCreek': {'lat': 135811.2276, 'lng': 521636.06376},
'Penrith': {'lat': 121567.60332, 'lng': 542466.62136},
'CoffsHarbour': {'lat': 135811.2276, 'lng': 521636.06376},
'Tuggeranong': {'lat': 127405.4454, 'lng': 537011.99568},
'Hobart': {'lat': 154390.11624, 'lng': 530367.69132},
'SalmonGums': {'lat': 135811.2276, 'lng': 521636.06376},
'WaggaWagga': {'lat': 126384.91092, 'lng': 530530.12008},
'Launceston': {'lat': 149157.42984, 'lng': 529685.52228},
'Williamtown': {'lat': 136194.38496, 'lng': 521785.3284},
'Nuriootpa': {'lat': 135811.2276, 'lng': 521636.06376},
'Canberra': {'lat': 126937.6704, 'lng': 536880.28248},
'Katherine': {'lat': 126595.97244, 'lng': 536862.31308},
'Perth': {'lat': 115018.79832, 'lng': 417102.99876}}
使用当前城市的中值填充其数字列
def drew_Histogram_of_fata(df,tag):
numerical = df._get_numeric_data().columns
# 绘图
fig, ax =plt.subplots(5,3, figsize=(30,35))
i=0;j=0;k=0
while i<=4:
while j<=2:
sns.distplot(df[numerical[k]], ax=ax[i, j])
j+=1;k+=1
j=0;i+=1;
plt.savefig(path+'处理缺省值'+tag+'分布.png')
plt.show()
处理前分布
drew_Histogram_of_fata(df=train,tag='前')
def median_fill_null(df):
# 获取是数字的列
numerical = df._get_numeric_data().columns
# 不是数字的列
categorical = set(df.columns) - set(numerical)
# 不同Location的序列
loc_for_miss = df["Location"].unique().tolist()
# 储存中值的数据结构
ls = []
j=0
while j<=len(numerical)-1:
for i in range(len(loc_for_miss)):
#大量异常值使用中值.median()
ls.append(str(df.loc[df["Location"] == loc_for_miss[i], numerical[j]].median()))
for i in range(len(loc_for_miss)):
df.loc[df["Location"] == loc_for_miss[i], numerical[j]] = df.loc[df["Location"] == \
loc_for_miss[i], numerical[j]].fillna(ls[i])
j+=1
df[numerical] = df[numerical].astype(float)
return {
'df':df,
'numerical':numerical,
'categorical':categorical,
'loc_for_miss':loc_for_miss
}
# 使用字典保存全局数据结构
dict_train = median_fill_null(train)
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/numpy/lib/nanfunctions.py:1111: RuntimeWarning: Mean of empty slice
return np.nanmean(a, axis, out=out, keepdims=keepdims)
处理空值后分布
drew_Histogram_of_fata(df=dict_train['df'],tag='后')
检查数字列异常值
箱型图特征分析
def drow_box_of_numerical(dict_data,isAfter=False):
num_of_rows = 4
num_of_cols = 4
df = dict_data['df']
numerical = dict_data['numerical']
fig, ax = plt.subplots(4, 4, figsize=(15,15))
i=0;j=0;k=0;
while i<num_of_rows:
while j<num_of_cols:
sns.boxplot(df[numerical[k]], ax=ax[i, j])
k+=1;j+=1
j=0;i+=1
if isAfter:
title = '去除数字列异常值后'
else:
title = '去除数字列异常值前'
plt.savefig(path+title+'.png')
plt.show()
处理前
drow_box_of_numerical(dict_data=dict_train,isAfter=False)
def removeOutliers(dict_data):
lsUpper = []
lsLower = []
df = dict_data['df']
numerical = dict_data['numerical']
for i in range(len(numerical)):
# .quantile(0.25)分布数
q1 = df[numerical[i]].quantile(0.25)
q3 = df[numerical[i]].quantile(0.75)
IQR = q3-q1
minimum = q1 - 1.5 * IQR
maximum = q3 + 1.5 * IQR
df.loc[(df[numerical[i]] <= minimum), numerical[i]] = minimum
df.loc[(df[numerical[i]] >= maximum), numerical[i]] = maximum
dict_data['df'] = df
return dict_data
dict_train = removeOutliers(dict_train)
处理后
len(dict_train['df'])
116368
drow_box_of_numerical(dict_data=dict_train,isAfter=True)
特征工程
#函数 特征工程
def feature_engineering(data):
# 将日期转换为年，月，日
data.insert(1, 'Year', 1, allow_duplicates=False)
data.insert(1, 'Month', 1, allow_duplicates=False)
data.insert(1, 'Day', 1, allow_duplicates=False)
for i in range(len(data['Date'])):
# 先转换为时间数组
try:
timeArray = time.strptime(data['Date'].iloc[i], "%Y-%m-%d")
except ValueError as e:
timeArray = time.strptime(data['Date'].iloc[i], "%Y/%m/%d")
data['Year'].iloc[i] =timeArray.tm_year
data['Month'].iloc[i] =timeArray.tm_mon
data['Day'].iloc[i] =timeArray.tm_mday
#将地点城市名称转为对应的经纬度
# 从json数据读取
with open(path+'city.json','r') as file_obj:
dic_Location = json.load(file_obj)
data.insert(1, 'lat', 1, allow_duplicates=False)
data.insert(1, 'lng', 1, allow_duplicates=False)
for i in range(len(data['Location'])):
item = data['Location'].iloc[i]
data['lat'].iloc[i] = dic_Location[item]['lat']
data['lng'].iloc[i] = dic_Location[item]['lng']
# 列补充与列删除
data = data.drop(columns=["WindGustDir", "WindDir9am", "WindDir3pm", "Location", "Date"])
data["AveTemp"] = (data["MinTemp"] + data["MaxTemp"]) / 2
data["WindSpeed12pm"] = (data["WindSpeed3pm"] + data["WindSpeed9am"]) / 2
data["Humidity12pm"] = (data["Humidity3pm"] + data["Humidity9am"]) / 2
data["Pressure12pm"] = (data["Pressure3pm"] + data["Pressure9am"]) / 2
data["Cloud12pm"] = (data["Cloud3pm"] + data["Cloud9am"]) / 2
data["Temp12am"] = (data["Temp3pm"] + data["Temp9am"]) / 2
# 将天气转化成int 填充空值为0
try:
data['RainTomorrow'].fillna('No', inplace = True)
data['RainTomorrow'] = data['RainTomorrow'].map({'No':0, 'Yes':1})
# 调整列序
df_RainTomorrow = data['RainTomorrow']
data = data.drop('RainTomorrow', axis=1)
data.insert(len(data.columns), 'RainTomorrow', df_RainTomorrow)
except:
pass
data['RainToday'].fillna('No', inplace = True)
data['RainToday'] = data['RainToday'].map({'No':0, 'Yes':1})
data.info()
# 返回值pandas类型
return data
# 将第一次处理后的数据存储
train = feature_engineering(dict_train['df'])
train.to_csv(path+"temp_train_1.csv",index=False)
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/pandas/core/indexing.py:670: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
iloc._setitem_with_indexer(indexer, value)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 116368 entries, 0 to 116367
Data columns (total 29 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 lng 116368 non-null float64
1 lat 116368 non-null float64
2 Day 116368 non-null int64
3 Month 116368 non-null int64
4 Year 116368 non-null int64
5 MinTemp 116368 non-null float64
6 MaxTemp 116368 non-null float64
7 Rainfall 116368 non-null float64
8 Evaporation 116368 non-null float64
9 Sunshine 116368 non-null float64
10 WindGustSpeed 116368 non-null float64
11 WindSpeed9am 116368 non-null float64
12 WindSpeed3pm 116368 non-null float64
13 Humidity9am 116368 non-null float64
14 Humidity3pm 116368 non-null float64
15 Pressure9am 116368 non-null float64
16 Pressure3pm 116368 non-null float64
17 Cloud9am 116368 non-null float64
18 Cloud3pm 116368 non-null float64
19 Temp9am 116368 non-null float64
20 Temp3pm 116368 non-null float64
21 RainToday 116368 non-null int64
22 AveTemp 116368 non-null float64
23 WindSpeed12pm 116368 non-null float64
24 Humidity12pm 116368 non-null float64
25 Pressure12pm 116368 non-null float64
26 Cloud12pm 116368 non-null float64
27 Temp12am 116368 non-null float64
28 RainTomorrow 116368 non-null int64
dtypes: float64(24), int64(5)
memory usage: 25.7 MB
train = pd.read_csv(path+"temp_train_1.csv")
train.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 116368 entries, 0 to 116367
Data columns (total 29 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 lng 116368 non-null float64
1 lat 116368 non-null float64
2 Day 116368 non-null int64
3 Month 116368 non-null int64
4 Year 116368 non-null int64
5 MinTemp 116368 non-null float64
6 MaxTemp 116368 non-null float64
7 Rainfall 116368 non-null float64
8 Evaporation 116368 non-null float64
9 Sunshine 116368 non-null float64
10 WindGustSpeed 116368 non-null float64
11 WindSpeed9am 116368 non-null float64
12 WindSpeed3pm 116368 non-null float64
13 Humidity9am 116368 non-null float64
14 Humidity3pm 116368 non-null float64
15 Pressure9am 116368 non-null float64
16 Pressure3pm 116368 non-null float64
17 Cloud9am 116368 non-null float64
18 Cloud3pm 116368 non-null float64
19 Temp9am 116368 non-null float64
20 Temp3pm 116368 non-null float64
21 RainToday 116368 non-null int64
22 AveTemp 116368 non-null float64
23 WindSpeed12pm 116368 non-null float64
24 Humidity12pm 116368 non-null float64
25 Pressure12pm 116368 non-null float64
26 Cloud12pm 116368 non-null float64
27 Temp12am 116368 non-null float64
28 RainTomorrow 116368 non-null int64
dtypes: float64(24), int64(5)
memory usage: 25.7 MB
找出效果最好的模型
# 分割测试集和训练集
train_n = np.asarray(train)
train_X = train_n[:,:-1].astype(float)
train_y = train_n[:,-1].astype(int)
train_X, test_X, train_y, test_y = train_test_split(train_X, train_y, train_size=0.7, random_state=0)
train_y
array([1, 0, 0, ..., 1, 0, 0])
得到f1
def get_f1(y_hat, y_true, THRESHOLD=0.5):
# '''
# y_hat是未经过sigmoid函数激活的
# 输出的f1为Marco-F1
# '''
epsilon = 1e-7
y_hat = y_hat>THRESHOLD
y_hat = np.int8(y_hat)
tp = np.sum(y_hat*y_true, axis=0)
fp = np.sum(y_hat*(1-y_true), axis=0)
fn = np.sum((1-y_hat)*y_true, axis=0)
p = tp/(tp+fp+epsilon)#epsilon的意义在于防止分母为0，否则当分母为0时python会报错
r = tp/(tp+fn+epsilon)
f1 = 2*p*r/(p+r+epsilon)
f1 = np.where(np.isnan(f1), np.zeros_like(f1), f1)
return np.mean(f1)
绘制ROC曲线
def plot_curve(y_hat, y_true, name):
fpr,tpr, threshold = roc_curve(y_hat, y_true)
f1 = get_f1(y_hat, y_true)
plt.figure()
lw = 2
plt.plot(fpr, tpr, color='darkorange', label='{} {}'.format(name,np.round(f1,2)))
plt.plot([0, 1], [0, 1], color='navy', linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('ROC Curve')
plt.legend(loc="lower right")
plt.savefig(path+'roc__'+name+'.png')
plt.show()
XGBClassifier
start = time.time()
model = XGBClassifier(
learning_rate =0.01,
n_estimators=346,
gamma=1.1345,
subsample=0.8,
reg_alpha=0.005,
objective= 'binary:logistic',
nthread=4,
scale_pos_weight=1,
seed=27
)
parameters={
'max_depth':range(2,10,1),
'min_child_weight':range(5, 21, 1),
'subsample':[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1],
'colsample_bytree':[0.5, 0.6, 0.7, 0.8, 0.9, 1],
'colsample_bylevel':[0.5, 0.6, 0.7, 0.8, 0.9, 1]
}
# 使用gpu
parameters['tree_method'] = ['gpu_hist']
method=GridSearchCV(estimator= model,param_grid=parameters,
cv=5,refit= True,scoring='f1_micro')
# method = XGBC(n_estimators=346,gamma=1.1345)
method.fit(train_X,train_y)
res = method.predict(test_X)
precision = 1-(np.sum(np.abs(res-test_y)) / len(res))
f1 = get_f1(res,test_y)
end = time.time()
print("运行时间 %.2f" %(end-start))
print("f1值 %.5f" %(f1))
# 保存模型
joblib.dump(method, path+'XGBClassifier.model')
['work/home/aistudio/data/data74989/XGBClassifier.model']
# 模型加载
method = joblib.load(path+'XGBClassifier.model')
res = method.predict(test_X)
precision = 1-(np.sum(np.abs(res-test_y)) / len(res))
f1 = get_f1(res,test_y)
print("f1值 %.5f" %(f1))
f1值 0.83815
plot_curve(res,test_y,'XGBClassifier')
随机森林回归
# 随机森林回归
method = RandomForestClassifier(bootstrap= False, criterion= 'entropy', min_samples_split= 4, n_estimators= 200, random_state=0)
start = time.time()
method.fit(train_X,train_y)
res = method.predict(test_X)
precision = 1-(np.sum(np.abs(res-test_y)) / len(res))
f1 = get_f1(res,test_y)
end = time.time()
print("运行时间 %.2f" %(end-start))
print("f1值 %.5f" %(f1))
运行时间 39.72
f1值 0.61063
plot_curve(res,test_y,'RandomForestClassifier')
线性回归
# 线性回归
method = LinearRegression()
start = time.time()
method.fit(train_X,train_y)
res = method.predict(test_X).astype('int')
precision = 1-(np.sum(np.abs(res-test_y)) / len(res))
f1 = get_f1(res,test_y)
end = time.time()
print("运行时间 %.2f" %(end-start))
print("f1值 %.5f" %(f1))
运行时间 0.12
f1值 0.01210
plot_curve(res,test_y,'LinearRegression')
SVC
# SVC
from sklearn.svm import SVC
# for kernel in ["linear","poly","rbf","sigmoid"]:
for kernel in ["linear"]:
method = SVC(kernel = kernel
,gamma="auto"
,degree = 1
,cache_size = 5000
,class_weight = {1:10} # "1"类的样本权重为10，隐含"0"类的样本权重为1
)
start = time.time()
method.fit(train_X,train_y)
res = method.predict(test_X)
precision = 1-(np.sum(np.abs(res-test_y)) / len(res))
f1 = get_f1(res,test_y)
end = time.time()
print("运行时间 %.2f" %(end-start))
print("f1值 %.5f" %(f1))
test_y
array([0, 0, 0, ..., 0, 1, 0])
逻辑斯蒂回归
# 逻辑斯蒂回归
method = LogisticRegression(max_iter=5000)
start = time.time()
method.fit(train_X,train_y)
res = method.predict(test_X).astype(int)
precision = 1-(np.sum(np.abs(res-test_y)) / len(res))
f1 = get_f1(res,test_y)
end = time.time()
print("运行时间 %.2f" %(end-start))
print("f1值 %.5f" %(f1))
运行时间 4.99
f1值 0.43874
plot_curve(res,test_y,'LogisticRegression')
梯度提升算法
# 梯度提升算法
from sklearn.ensemble import GradientBoostingRegressor
start = time.time()
method = GradientBoostingRegressor(n_estimators=100, warm_start=True,learning_rate=0.01, max_depth=10, random_state=0, loss='ls')
method.fit(train_X,train_y)
res = method.predict(test_X)
precision = 1-(np.sum(np.abs(res-test_y)) / len(res))
f1 = get_f1(res,test_y)
end = time.time()
print("运行时间 %.2f" %(end-start))
print("f1值 %.5f" %(f1))
运行时间 151.53
f1值 0.48708
plot_curve(res,test_y,'GradientBoostingRegressor')
使用决策树解决回归问题
#使用决策树解决回归问题
from sklearn.tree import DecisionTreeClassifier
# 找出f1值最大的
f1 = 0
ind = 0
start = time.time()
for i in range(1,15):
method=DecisionTreeClassifier(max_depth=i)
method.fit(train_X,train_y)
res = method.predict(test_X)
precision = 1-(np.sum(np.abs(res-test_y)) / len(res))
if get_f1(res,test_y) > f1:
f1 = get_f1(res,test_y)
ind = i
end = time.time()
print("运行时间 %.2f" %(end-start))
print('最大的'+"f1值 %.5f max_depth=%d" %(f1,ind))
运行时间 16.08
最大的f1值 0.57431 max_depth=9
plot_curve(res,test_y,'DecisionTreeClassifier')
贝叶斯
# 贝叶斯
from sklearn.naive_bayes import GaussianNB
method = GaussianNB()
start = time.time()
method.fit(train_X,train_y)
res = method.predict(test_X).astype(int)
precision = 1-(np.sum(np.abs(res-test_y)) / len(res))
f1 = get_f1(res,test_y)
end = time.time()
print("运行时间 %.2f" %(end-start))
print("f1值 %.5f" %(f1))
运行时间 0.06
f1值 0.56476
plot_curve(res,test_y,'GaussianNB')
使用效果最好的模型进行预测
# 数据集准备
test = pd.read_csv(path+'test.csv')
dict_test = median_fill_null(df=test)
dict_test = removeOutliers(dict_data=dict_test)
temp_test = dict_test['df'].copy()
ntest = feature_engineering(temp_test)
ntest.to_csv(path+"new_test.csv",index=False)
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/numpy/lib/nanfunctions.py:1111: RuntimeWarning: Mean of empty slice
return np.nanmean(a, axis, out=out, keepdims=keepdims)
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/pandas/core/indexing.py:670: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
iloc._setitem_with_indexer(indexer, value)
ntest = pd.read_csv(path+'new_test.csv')
ntest.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 29092 entries, 0 to 29091
Data columns (total 28 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 lng 29092 non-null float64
1 lat 29092 non-null float64
2 Day 29092 non-null int64
3 Month 29092 non-null int64
4 Year 29092 non-null int64
5 MinTemp 29092 non-null float64
6 MaxTemp 29092 non-null float64
7 Rainfall 29092 non-null float64
8 Evaporation 29092 non-null float64
9 Sunshine 29092 non-null float64
10 WindGustSpeed 29092 non-null float64
11 WindSpeed9am 29092 non-null float64
12 WindSpeed3pm 29092 non-null float64
13 Humidity9am 29092 non-null float64
14 Humidity3pm 29092 non-null float64
15 Pressure9am 29092 non-null float64
16 Pressure3pm 29092 non-null float64
17 Cloud9am 29092 non-null float64
18 Cloud3pm 29092 non-null float64
19 Temp9am 29092 non-null float64
20 Temp3pm 29092 non-null float64
21 RainToday 29092 non-null int64
22 AveTemp 29092 non-null float64
23 WindSpeed12pm 29092 non-null float64
24 Humidity12pm 29092 non-null float64
25 Pressure12pm 29092 non-null float64
26 Cloud12pm 29092 non-null float64
27 Temp12am 29092 non-null float64
dtypes: float64(24), int64(4)
memory usage: 6.2 MB
nntest = np.asarray(ntest)
res = method.predict(nntest).astype(int)
res
array([0, 0, 0, ..., 0, 0, 0])
len(res)
29092
生成结果并保存
# 生成结果并保存
import datetime
last_res = pd.read_csv(path+'test.csv')
last_res = pd.DataFrame(last_res['Date'])
last_res.insert(1, 'RainTomorrow', res, allow_duplicates=False)
last_res['RainTomorrow'] = last_res['RainTomorrow'].astype(int)
last_res['RainTomorrow'] = last_res['RainTomorrow'].map({1:'Yes',0:'No'})
last_res.to_csv(path+"0423.csv",index=False)
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