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predict.py
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predict.py
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# -*- coding: utf-8 -*-
"""Untitled2.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1TrSNWRt8oaqMIx8rWDVfC6FBFPdFdnZD
"""
from sklearn.cross_validation import train_test_split
import pandas as pd
import numpy as np
import datetime
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.cross_validation import train_test_split
from sklearn.neighbors import KNeighborsClassifier
#Data considered to be in DataFrame Form
#Data in columns TIMESTAMP,CATEGORY,LATITUDE,LONGITUDE
#time date to TIMESTAMP FORM - 2 lines
#HotOneEncode the category column
#pd.get_dummies(df)
def train(data):
dataset=data.copy()
column_1 = data.ix[:,0]
db=pd.DataFrame({"year": column_1.dt.year,
"month": column_1.dt.month,
"day": column_1.dt.day,
"hour": column_1.dt.hour,
"dayofyear": column_1.dt.dayofyear,
"week": column_1.dt.week,
"weekofyear": column_1.dt.weekofyear,
"dayofweek": column_1.dt.dayofweek,
"weekday": column_1.dt.weekday,
"quarter": column_1.dt.quarter,
})
dataset1=dataset.drop('timestamp',axis=1)
data1=pd.concat([db,dataset1],axis=1)
data1.dropna(inplace=True)
X=data1.iloc[:,[1,2,3,4,6,16,17]].values
y=data1.iloc[:,[10,11,12,13,14,15]].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.0, random_state=50)
knn = KNeighborsClassifier(n_neighbors=11)
knn.fit(X_train,y_train)
return knn
def predict(knn,pred):
pred[0][0]= datetime.datetime.strptime(pred[0][0], '%Y-%m-%d %H:%M:%S.%f')
week=pred[0][0].dt.week
month=pred[0][0].dt.month
hour=pred[0][0].dt.hour
doy=pred[0][0].dt.dayofyear
dow=pred[0][0].dt.dayofweek
col1=pred[0][1:]
pred[0].insert(0,week)
pred[0].insert(0,month)
pred[0].insert(0,hour)
pred[0].insert(0,doy)
pred[0].insert(0,dow)
print (pred)
result=knn.predict(pred)
label=list(result[0]).index(1)
if label==0:
print ("Physical Abuse")
elif label==1:
print("Sexual Abuse")
elif label==2:
print("Emotional Abuse")
elif label==3:
print("Financial Abuse")
elif label==4:
print("Criminal Harassment")
else:
print("Other")
k=train(data)
predict(k,[[2018-02-28 10:15:00 , 22.769531,
75.888772]])
x=[[ 2. , 59. , 10. , 2. , 9. , 22.769531,
75.888772]]
x[1].insert(0,3)
x
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.cross_validation import train_test_split
from sklearn.neighbors import KNeighborsClassifier
data=pd.read_csv('data3.csv')
dataset=pd.read_csv('data3.csv')
data.head()
#data = data.rename(columns={'act379': 'Physical', 'act13': 'Sexual', 'act279': 'Emotional', 'act323': 'Financial', 'act363': 'Criminal', 'act302': 'Other'})
column_1 = data.ix[:,0]
db=pd.DataFrame({"year": column_1.dt.year,
"month": column_1.dt.month,
"day": column_1.dt.day,
"hour": column_1.dt.hour,
"dayofyear": column_1.dt.dayofyear,
"week": column_1.dt.week,
"weekofyear": column_1.dt.weekofyear,
"dayofweek": column_1.dt.dayofweek,
"weekday": column_1.dt.weekday,
"quarter": column_1.dt.quarter,
})
data['timestamp'] = pd.to_datetime(data['timestamp'], errors='coerce')
data['timestamp'] = pd.to_datetime(data['timestamp'], format = '%d/%m/%Y %H:%M:%S')
#data.to_csv('data3.csv', index=False)
data.head()
dataset1=dataset.drop('timestamp',axis=1)
dataset1.head()
data1=pd.concat([db,dataset1],axis=1)
data1.head()
data1.info()
data1.dropna(inplace=True)
data1.head()
X=data1.iloc[:,[1,2,3,4,6,16,17]].values
len(X)
y=data1.iloc[:,[10,11,12,13,14,15]].values
y[1]
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=50)
len(X_train)
knn = KNeighborsClassifier(n_neighbors=11)
knn.fit(X_train,y_train)
knn.score(X_test,y_test)
result=knn.predict([[ 2. , 59. , 15. , 2. , 9. , 22.723873,
75.828416]])
result
label=list(result[0]).index(1)
if label==0:
print ("Physical Abuse")
elif label==1:
print("Sexual Abuse")
elif label==2:
print("Emotional Abuse")
elif label==3:
print("Financial Abuse")
elif label==4:
print("Criminal Harassment")
else:
print("Other")
from sklearn.tree import DecisionTreeClassifier
dt = DecisionTreeClassifier(max_depth=50, random_state=100)
dt.fit(X_train,y_train)
y_pred=dt.predict(X_test)
len(y_pred)
len(X_test)
y_pred
data1.dtypes.index
#Physical Abuse,Sexual Abuse,Financial Abuse,Emotional Abuse,Criminal Harassment, Other
y_test
knn.score(X_test,y_test)
df = pd.DataFrame({'country': ['russia', 'germany', 'australia','korea','germany']})
df["col2"]=[1,2,34,5,5]
df
df.dtypes
pd.get_dummies(df)