import pandas;
from pandas import read_csv;
data = read_csv(
'D:\\PDM\\4.5\\data.csv',
encoding='utf8'
)
data = data.dropna()
dummyColumns = [
'Gender', 'Home Ownership', 'Internet Connection', 'Marital Status',
'Movie Selector', 'Prerec Format', 'TV Signal']
for column in dummyColumns:
data[column]=data[column].astype('category')
dummiesData = pandas.get_dummies(
data,
columns=dummyColumns,
prefix=dummyColumns,
prefix_sep=" ",
drop_first=True
)
"""
博士后? ? Post-Doc
博士? ? ? Doctorate
碩士? ? ? Master's Degree
學(xué)士? ? ? Bachelor's Degree
副學(xué)士? ? Associate's Degree
專業(yè)院校? Some College
職業(yè)學(xué)校? Trade School
高中? ? ? High School
小學(xué)? ? ? Grade School
"""
educationLevelDict = {
'Post-Doc': 9,
'Doctorate': 8,
'Master\'s Degree': 7,
'Bachelor\'s Degree': 6,
'Associate\'s Degree': 5,
'Some College': 4,
'Trade School': 3,
'High School': 2,
'Grade School': 1
}
dummiesData['Education Level Map'] = dummiesData['Education Level'].map(educationLevelDict)
freqMap = {
'Never': 0,
'Rarely': 1,
'Monthly': 2,
'Weekly': 3,
'Daily': 4
}
dummiesData['PPV Freq Map'] = dummiesData['PPV Freq'].map(freqMap)
dummiesData['Theater Freq Map'] = dummiesData['Theater Freq'].map(freqMap)
dummiesData['TV Movie Freq Map'] = dummiesData['TV Movie Freq'].map(freqMap)
dummiesData['Prerec Buying Freq Map'] = dummiesData['Prerec Buying Freq'].map(freqMap)
dummiesData['Prerec Renting Freq Map'] = dummiesData['Prerec Renting Freq'].map(freqMap)
dummiesData['Prerec Viewing Freq Map'] = dummiesData['Prerec Viewing Freq'].map(freqMap)
dummiesSelect = [
'Age', 'Num Bathrooms', 'Num Bedrooms', 'Num Cars', 'Num Children', 'Num TVs',
'Education Level Map', 'PPV Freq Map', 'Theater Freq Map', 'TV Movie Freq Map',
'Prerec Buying Freq Map', 'Prerec Renting Freq Map', 'Prerec Viewing Freq Map',
'Gender Male',
'Internet Connection DSL', 'Internet Connection Dial-Up',
'Internet Connection IDSN', 'Internet Connection No Internet Connection',
'Internet Connection Other',
'Marital Status Married', 'Marital Status Never Married',
'Marital Status Other', 'Marital Status Separated',
'Movie Selector Me', 'Movie Selector Other', 'Movie Selector Spouse/Partner',
'Prerec Format DVD', 'Prerec Format Laserdisk', 'Prerec Format Other',
'Prerec Format VHS', 'Prerec Format Video CD',
'TV Signal Analog antennae', 'TV Signal Cable',
'TV Signal Digital Satellite', 'TV Signal Don\'t watch TV'
]
inputData = dummiesData[dummiesSelect]
outputData = dummiesData[['Home Ownership Rent']]
from sklearn.neural_network import MLPClassifier
for l in range(1, 11):
ANNModel = MLPClassifier(
activation='relu',
hidden_layer_sizes=l
)
ANNModel.fit(inputData, outputData)
score = ANNModel.score(inputData, outputData)
print(str(l) + ", " + str(score))
newData = read_csv(
'D:\\PDM\\4.4\\newData.csv',
encoding='utf8'
)
for column in dummyColumns:
newData[column] = newData[column].astype(
'category',
categories=data[column].cat.categories
)
newData = newData.dropna()
newData['Education Level Map'] = newData['Education Level'].map(educationLevelDict)
newData['PPV Freq Map'] = newData['PPV Freq'].map(freqMap)
newData['Theater Freq Map'] = newData['Theater Freq'].map(freqMap)
newData['TV Movie Freq Map'] = newData['TV Movie Freq'].map(freqMap)
newData['Prerec Buying Freq Map'] = newData['Prerec Buying Freq'].map(freqMap)
newData['Prerec Renting Freq Map'] = newData['Prerec Renting Freq'].map(freqMap)
newData['Prerec Viewing Freq Map'] = newData['Prerec Viewing Freq'].map(freqMap)
dummiesNewData = pandas.get_dummies(
newData,
columns=dummyColumns,
prefix=dummyColumns,
prefix_sep=" ",
drop_first=True
)
inputNewData = dummiesNewData[dummiesSelect]
ANNModel.predict(inputData)
