将PyMC2代码移植到PyMC3层次模型进行体育分析

2024-03-29 00:30:36 发布

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我尝试了以下代码,但遇到了问题。 我想。值是个问题,但我如何将它编码为一个Theano对象?在

以下是我的数据源

home_team,away_team,home_score,away_score
Wales,Italy,23,15
France,England,26,24
Ireland,Scotland,28,6
Ireland,Wales,26,3
Scotland,England,0,20
France,Italy,30,10
Wales,France,27,6
Italy,Scotland,20,21
England,Ireland,13,10
Ireland,Italy,46,7
Scotland,France,17,19
England,Wales,29,18
Italy,England,11,52
Wales,Scotland,51,3
France,Ireland,20,22

下面是PyMC2代码,它可以工作: data_file=data_DIR+'结果\u 2014.csv'

^{pr2}$

我尝试移植到PyMC3:) 我还包括了争吵的代码。 我定义了自己的数据目录等

data_file = DATA_DIR + 'results_2014.csv'

df = pd.read_csv(data_file, sep=',')
# Or whatever it takes to get this into a data frame.
teams = df.home_team.unique()
teams = pd.DataFrame(teams, columns=['team'])
teams['i'] = teams.index
df = pd.merge(df, teams, left_on='home_team', right_on='team', how='left')
df = df.rename(columns = {'i': 'i_home'}).drop('team', 1)
df = pd.merge(df, teams, left_on='away_team', right_on='team', how='left')
df = df.rename(columns = {'i': 'i_away'}).drop('team', 1)
observed_home_goals = df.home_score.values
observed_away_goals = df.away_score.values
home_team = df.i_home.values
away_team = df.i_away.values
num_teams = len(df.i_home.drop_duplicates())
num_games = len(home_team)
g = df.groupby('i_away')
att_starting_points = np.log(g.away_score.mean())
g = df.groupby('i_home')
def_starting_points = -np.log(g.away_score.mean())

import theano.tensor as T
import pymc3 as pm3
#hyperpriors


x = att_starting_points.values
y = def_starting_points.values
model = pm.Model()
with pm3.Model() as model:
    home3 = pm3.Normal('home', 0, .0001)
    tau_att3 = pm3.Gamma('tau_att', .1, .1)
    tau_def3 = pm3.Gamma('tau_def', .1, .1)
    intercept3 = pm3.Normal('intercept', 0, .0001)
    #team-specific parameters
    atts_star3 = pm3.Normal("atts_star", 
                        mu=0, 
                        tau=tau_att3, 
                        observed=x)
    defs_star3 = pm3.Normal("defs_star", 
                        mu=0, 
                        tau=tau_def3,  
                        observed=y) 
    #Seems to be the error here. 
    atts = pm3.Deterministic('regression', 
    atts_star3 - np.mean(atts_star3))
    home_theta3 = pm3.Deterministic('regression', 
    T.exp(intercept3 + atts[away_team] + defs[home_team]))
atts = pm3.Deterministic('regression', atts_star3 - np.mean(atts_star3))
    home_theta3 = pm3.Deterministic('regression', T.exp(intercept3 +     atts[away_team] + defs[home_team]))
    # Unknown model parameters
    home_points3 = pm3.Poisson('home_points', mu=home_theta3, observed=observed_home_goals)
    away_points3 = pm3.Poisson('away_points', mu=home_theta3, observed=observed_away_goals)
    start = pm3.find_MAP()
    step = pm3.NUTS(state=start)
    trace = pm3.sample(2000, step, start=start, progressbar=True)

    pm3.traceplot(trace)

我得到一个错误,比如值不是Theano对象。 我想这就是上面的价值观部分。但我不知道如何把它转换成Theano张量。张量让我很困惑:)

为了清楚起见,这个错误是因为我误解了PyMC3语法中的某些内容。在

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-71-ce51c1a64412> in <module>()
     23 
     24     #Seems to be the error here.
---> 25     atts = pm3.Deterministic('regression', atts_star3 - np.mean(atts_star3))
     26     home_theta3 = pm3.Deterministic('regression', T.exp(intercept3 + atts[away_team] + defs[home_team]))
     27 

/Users/peadarcoyle/anaconda/lib/python3.4/site-packages/numpy/core/fromnumeric.py in mean(a, axis, dtype, out, keepdims)
   2733 
   2734     return _methods._mean(a, axis=axis, dtype=dtype,
-> 2735                             out=out, keepdims=keepdims)
   2736 
   2737 def std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):

/Users/peadarcoyle/anaconda/lib/python3.4/site-packages/numpy/core/_methods.py in _mean(a, axis, dtype, out, keepdims)
     71         ret = ret.dtype.type(ret / rcount)
     72     else:
---> 73         ret = ret / rcount
     74 
     75     return ret

TypeError: unsupported operand type(s) for /: 'ObservedRV' and 'int'

Tags: dfhomemeanteampointsscorevaluestau
3条回答

以下是我对您的PyMC2模型的翻译:

model = pm.Model()
with pm.Model() as model:
    # global model parameters
    home        = pm.Normal('home',      0, .0001)
    tau_att     = pm.Gamma('tau_att',   .1, .1)
    tau_def     = pm.Gamma('tau_def',   .1, .1)
    intercept   = pm.Normal('intercept', 0, .0001)

    # team-specific model parameters
    atts_star   = pm.Normal("atts_star", 
                           mu   =0,
                           tau  =tau_att, 
                           shape=num_teams)
    defs_star   = pm.Normal("defs_star", 
                           mu   =0,
                           tau  =tau_def,  
                           shape=num_teams)

    atts        = pm.Deterministic('atts', atts_star - tt.mean(atts_star))
    defs        = pm.Deterministic('defs', defs_star - tt.mean(defs_star))
    home_theta  = tt.exp(intercept + home + atts[home_team] + defs[away_team]
    away_theta  = tt.exp(intercept + atts[away_team] + defs[home_team])

    # likelihood of observed data
    home_points = pm.Poisson('home_points', mu=home_theta, observed=observed_home_goals)
    away_points = pm.Poisson('away_points', mu=away_theta, observed=observed_away_goals)

在我看来,PyMC2和3模型构建之间的最大区别是PyMC2中初始值的整个业务不包括在PyMC3的模型构建中。它被推到代码的模型拟合部分。在

这里有一个笔记本,它将此模型与您的数据和一些合适的代码放在上下文中:http://nbviewer.ipython.org/gist/aflaxman/55e23195fe0a0b089103

你的模型失败了,因为你不能在no张量上使用NumPy函数。因此

np.mean(atts_star3)

会给你一个错误。您可以删除atts_star3 = pm3.Normal("atts_star",...)并直接使用NumPy数组atts_star3 = x。在

我认为您不需要显式地建模tau_att3tau_def3或{}。在

或者,如果希望保留这些变量,可以将np.mean替换为theano.tensor.mean,这应该可以。在

所以我就这么做了。它不是我以前版本的直接端口,但它给了我一个答案。有人有什么反馈吗?在

import os
import math
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pymc3 as pm3# I know folks are switching to "as pm" but I'm just not there yet
%matplotlib inline
import seaborn as sns
from IPython.core.pylabtools import figsize
import seaborn as sns
import theano.tensor as T
figsize(12, 12)
DATA_DIR = os.path.join(os.getcwd(), 'data/')
data_file = DATA_DIR + 'results_2014.csv'

df = pd.read_csv(data_file, sep=',')
# Or whatever it takes to get this into a data frame.
teams = df.home_team.unique()
teams = pd.DataFrame(teams, columns=['team'])
teams['i'] = teams.index
df = pd.merge(df, teams, left_on='home_team', right_on='team', how='left')
df = df.rename(columns = {'i': 'i_home'}).drop('team', 1)
df = pd.merge(df, teams, left_on='away_team', right_on='team', how='left')
df = df.rename(columns = {'i': 'i_away'}).drop('team', 1)
observed_home_goals = df.home_score.values
observed_away_goals = df.away_score.values
home_team = df.i_home.values
away_team = df.i_away.values
num_teams = len(df.i_home.drop_duplicates())
num_games = len(home_team)
g = df.groupby('i_away')
att_starting_points = np.log(g.away_score.mean())
g = df.groupby('i_home')
def_starting_points = -np.log(g.away_score.mean())

import theano.tensor as T
import pymc3 as pm3
#hyperpriors

'''
def atts3(atts_star3=atts_star3):
    atts3 = atts_star.copy()
    atts3 = atts3 - np.mean(atts_star)
    return atts3
def defs3(defs_star3=defs_star3):
    defs3 = defs_star3.copy()
    defs3 = defs3 - np.mean(defs_star3)
    return defs
    '''
model = pm3.Model()
with pm3.Model() as model:
    home3 = pm3.Normal('home', 0, .0001)
    tau_att3 = pm3.Gamma('tau_att', .1, .1)
    tau_def3 = pm3.Gamma('tau_def', .1, .1)
    intercept3 = pm3.Normal('intercept', 0, .0001)
    #team-specific parameters
    atts_star3 = pm3.Normal("atts_star", 
                        mu=0, 
                        tau=tau_att3, 
                        shape=num_teams, 
                        observed=att_starting_points.values)
    defs_star3 = pm3.Normal("defs_star", 
                        mu=0, 
                        tau=tau_def3, 
                        shape=num_teams, 
                        observed=def_starting_points.values) 


    #home_theta3 = atts3 + defs3
    #away_theta3 = atts3 + defs3
    # Unknown model parameters
    home_points3 = pm3.Poisson('home_points', mu=1, observed=observed_home_goals)
    away_points3 = pm3.Poisson('away_points', mu=1, observed=observed_away_goals)
    start = pm3.find_MAP()
    step = pm3.NUTS(state=start)
    trace = pm3.sample(2000, step, start=start, progressbar=True)

    pm3.traceplot(trace)

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