AS3-1 Group-4 Popularity of music records

rm(list=ls(all=T))
options(digits=4, scipen=12)
library(dplyr); library(ggplot2)

Introduction

議題：使用歌曲的屬性，預測它會不會進入流行歌曲排行榜的前10名

學習重點：

依時間分割資料
model formula 的寫法
高相關(共線性)自變數之間的選擇
accuracy, sensitivity, specificity的實際意義
如何調整臨界機率來權衡：TFR/sensitivity vs. FPR/specificity

1 基本的資料處理 Understanding the Data

【1.1】How many observations (songs) are from the year 2010?

setwd("C:/MIT summer 2018/Unit3/data")

The working directory was changed to C:/MIT summer 2018/Unit3/data inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.

Song = read.csv("songs.csv")
sum(Song$year==2010)

[1] 373

【1.2】How many songs does the dataset include for which the artist name is “Michael Jackson”?

sum(Song$artistname=="Michael Jackson")

[1] 18

【1.3】Which of these songs by Michael Jackson made it to the Top 10? Select all that apply.

A = subset(Song,artistname=="Michael Jackson")
A$songtitle[which(A$Top10==1)]

[1] You Rock My World You Are Not Alone Black or White    Remember the Time In The Closet    
7141 Levels: \x91u_ Creias? _\x84\x8d '03 Bonnie & Clyde '69 \x91iva Vlad!  ... Zumbi

#You Rock My World , You Are Not Alone

【1.4】(a) What are the values of timesignature that occur in our dataset? (b) Which timesignature value is the most frequent among songs in our dataset?

summary(Song$timesignature)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   0.00    4.00    4.00    3.89    4.00    7.00

table(Song$timesignature)


   0    1    3    4    5    7 
  10  143  503 6787  112   19

#0    1    3    4    5    7 
#4

【1.5】 Which of the following songs has the highest tempo?

Song$songtitle[which.max(Song$tempo)]

[1] Wanna Be Startin' Somethin'
7141 Levels: \x91u_ Creias? _\x84\x8d '03 Bonnie & Clyde '69 \x91iva Vlad!  ... Zumbi

2 建立模型 Creating Our Prediction Model

【2.1 依時間分割資料】How many observations (songs) are in the training set?

SongsTrain = subset(Song,year<="2009")
SongsTest = subset(Song,year>"2009")
nrow(SongsTrain)

[1] 7201

【2.2 建立模型、模型摘要】What is the value of the Akaike Information Criterion (AIC)?

nonvars = c("year", "songtitle", "artistname", "songID", "artistID")
SongsTrain = SongsTrain[ , !(names(SongsTrain) %in% nonvars) ]
SongsTest = SongsTest[ ,!(names(SongsTest) %in% nonvars) ]
SongsLog1 = glm(Top10~.,data =SongsTrain,family = binomial)
summary(SongsLog1)


Call:
glm(formula = Top10 ~ ., family = binomial, data = SongsTrain)

Deviance Residuals: 
   Min      1Q  Median      3Q     Max  
-1.922  -0.540  -0.346  -0.184   3.077  

Coefficients:
                            Estimate  Std. Error z value           Pr(>|z|)    
(Intercept)               14.6999882   1.8063875    8.14 0.0000000000000004 ***
timesignature              0.1263948   0.0867357    1.46            0.14505    
timesignature_confidence   0.7449923   0.1953053    3.81            0.00014 ***
loudness                   0.2998794   0.0291654   10.28            < 2e-16 ***
tempo                      0.0003634   0.0016915    0.21            0.82989    
tempo_confidence           0.4732270   0.1421740    3.33            0.00087 ***
key                        0.0158820   0.0103895    1.53            0.12635    
key_confidence             0.3086751   0.1411562    2.19            0.02876 *  
energy                    -1.5021445   0.3099240   -4.85 0.0000012545913310 ***
pitch                    -44.9077399   6.8348831   -6.57 0.0000000000501890 ***
timbre_0_min               0.0231589   0.0042562    5.44 0.0000000529331342 ***
timbre_0_max              -0.3309820   0.0256926  -12.88            < 2e-16 ***
timbre_1_min               0.0058810   0.0007798    7.54 0.0000000000000464 ***
timbre_1_max              -0.0002449   0.0007152   -0.34            0.73209    
timbre_2_min              -0.0021274   0.0011260   -1.89            0.05884 .  
timbre_2_max               0.0006586   0.0009066    0.73            0.46757    
timbre_3_min               0.0006920   0.0005985    1.16            0.24758    
timbre_3_max              -0.0029673   0.0005815   -5.10 0.0000003344570390 ***
timbre_4_min               0.0103956   0.0019850    5.24 0.0000001632385067 ***
timbre_4_max               0.0061105   0.0015503    3.94 0.0000809670432888 ***
timbre_5_min              -0.0055980   0.0012767   -4.38 0.0000116146773897 ***
timbre_5_max               0.0000774   0.0007935    0.10            0.92234    
timbre_6_min              -0.0168562   0.0022640   -7.45 0.0000000000000966 ***
timbre_6_max               0.0036681   0.0021895    1.68            0.09388 .  
timbre_7_min              -0.0045492   0.0017815   -2.55            0.01066 *  
timbre_7_max              -0.0037737   0.0018320   -2.06            0.03941 *  
timbre_8_min               0.0039110   0.0028510    1.37            0.17012    
timbre_8_max               0.0040113   0.0030030    1.34            0.18162    
timbre_9_min               0.0013673   0.0029981    0.46            0.64836    
timbre_9_max               0.0016027   0.0024336    0.66            0.51019    
timbre_10_min              0.0041263   0.0018391    2.24            0.02485 *  
timbre_10_max              0.0058250   0.0017694    3.29            0.00099 ***
timbre_11_min             -0.0262523   0.0036933   -7.11 0.0000000000011760 ***
timbre_11_max              0.0196734   0.0033855    5.81 0.0000000062068661 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 6017.5  on 7200  degrees of freedom
Residual deviance: 4759.2  on 7167  degrees of freedom
AIC: 4827

Number of Fisher Scoring iterations: 6

【2.3 模型係數判讀】The LOWER or HIGHER our confidence about time signature, key and tempo, the more likely the song is to be in the Top 10

# time signature -> HIGHER
# key -> HIGHER
# tempo -> HIGHER

【2.4 進行推論】What does Model 1 suggest in terms of complexity?

# Mainstream listeners tend to prefer less complex songs

【2.5 檢查異常係數】 (a) By inspecting the coefficient of the variable “loudness”, what does Model 1 suggest? (b) By inspecting the coefficient of the variable “energy”, do we draw the same conclusions as above?

# Mainstream listeners prefer songs with heavy instrumentation
# No

3 處理共線性 Beware of Multicollinearity Issues!

【3.1 檢查相關係數】What is the correlation between loudness and energy in the training set?

cor(SongsTrain$loudness,SongsTrain$energy)

[1] 0.7399

【3.2 重新建立模型、檢查係數】Look at the summary of SongsLog2, and inspect the coefficient of the variable “energy”. What do you observe?

SongsLog2 = glm(Top10 ~ . - loudness, data=SongsTrain, family=binomial)
summary(SongsLog2)


Call:
glm(formula = Top10 ~ . - loudness, family = binomial, data = SongsTrain)

Deviance Residuals: 
   Min      1Q  Median      3Q     Max  
-2.098  -0.561  -0.360  -0.190   3.311  

Coefficients:
                           Estimate Std. Error z value           Pr(>|z|)    
(Intercept)               -2.240612   0.746484   -3.00            0.00269 ** 
timesignature              0.162461   0.087341    1.86            0.06287 .  
timesignature_confidence   0.688471   0.192419    3.58            0.00035 ***
tempo                      0.000552   0.001665    0.33            0.74023    
tempo_confidence           0.549657   0.140736    3.91 0.0000940005473689 ***
key                        0.017403   0.010256    1.70            0.08974 .  
key_confidence             0.295367   0.139446    2.12            0.03416 *  
energy                     0.181260   0.260768    0.70            0.48699    
pitch                    -51.498579   6.856544   -7.51 0.0000000000000587 ***
timbre_0_min               0.024789   0.004240    5.85 0.0000000050055433 ***
timbre_0_max              -0.100697   0.011776   -8.55            < 2e-16 ***
timbre_1_min               0.007143   0.000771    9.27            < 2e-16 ***
timbre_1_max              -0.000783   0.000706   -1.11            0.26765    
timbre_2_min              -0.001579   0.001109   -1.42            0.15453    
timbre_2_max               0.000389   0.000896    0.43            0.66443    
timbre_3_min               0.000650   0.000595    1.09            0.27452    
timbre_3_max              -0.002462   0.000567   -4.34 0.0000143015554481 ***
timbre_4_min               0.009115   0.001952    4.67 0.0000030176578261 ***
timbre_4_max               0.006306   0.001532    4.12 0.0000387139806484 ***
timbre_5_min              -0.005641   0.001255   -4.50 0.0000069522013076 ***
timbre_5_max               0.000694   0.000781    0.89            0.37426    
timbre_6_min              -0.016122   0.002235   -7.21 0.0000000000005452 ***
timbre_6_max               0.003814   0.002157    1.77            0.07698 .  
timbre_7_min              -0.005102   0.001755   -2.91            0.00364 ** 
timbre_7_max              -0.003158   0.001811   -1.74            0.08109 .  
timbre_8_min               0.004488   0.002810    1.60            0.11025    
timbre_8_max               0.006422   0.002950    2.18            0.02950 *  
timbre_9_min              -0.000428   0.002955   -0.14            0.88479    
timbre_9_max               0.003525   0.002377    1.48            0.13802    
timbre_10_min              0.002993   0.001804    1.66            0.09700 .  
timbre_10_max              0.007367   0.001731    4.25 0.0000209292079939 ***
timbre_11_min             -0.028370   0.003630   -7.82 0.0000000000000055 ***
timbre_11_max              0.018294   0.003341    5.48 0.0000000434235974 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 6017.5  on 7200  degrees of freedom
Residual deviance: 4871.8  on 7168  degrees of freedom
AIC: 4938

Number of Fisher Scoring iterations: 6

# Model 2 suggests that songs with high energy levels tend to be more popular. This contradicts our observation in Model 1.

【3.3 選擇模型】 do we make the same observation about the popularity of heavy instrumentation as we did with Model 2?

SongsLog3 = glm(Top10 ~ . - energy, data=SongsTrain, family=binomial)
summary(SongsLog3)


Call:
glm(formula = Top10 ~ . - energy, family = binomial, data = SongsTrain)

Deviance Residuals: 
   Min      1Q  Median      3Q     Max  
-1.918  -0.542  -0.348  -0.187   3.417  

Coefficients:
                            Estimate  Std. Error z value           Pr(>|z|)    
(Intercept)               11.9605621   1.7141947    6.98 0.0000000000030077 ***
timesignature              0.1150942   0.0872615    1.32            0.18718    
timesignature_confidence   0.7142698   0.1946175    3.67            0.00024 ***
loudness                   0.2305565   0.0252798    9.12            < 2e-16 ***
tempo                     -0.0006460   0.0016655   -0.39            0.69811    
tempo_confidence           0.3840930   0.1398350    2.75            0.00602 ** 
key                        0.0164946   0.0103514    1.59            0.11106    
key_confidence             0.3394064   0.1408744    2.41            0.01598 *  
pitch                    -53.2840575   6.7328544   -7.91 0.0000000000000025 ***
timbre_0_min               0.0220452   0.0042394    5.20 0.0000001992236315 ***
timbre_0_max              -0.3104800   0.0253654  -12.24            < 2e-16 ***
timbre_1_min               0.0054160   0.0007643    7.09 0.0000000000013757 ***
timbre_1_max              -0.0005115   0.0007110   -0.72            0.47193    
timbre_2_min              -0.0022544   0.0011203   -2.01            0.04419 *  
timbre_2_max               0.0004119   0.0009020    0.46            0.64791    
timbre_3_min               0.0003179   0.0005869    0.54            0.58808    
timbre_3_max              -0.0029637   0.0005758   -5.15 0.0000002640646649 ***
timbre_4_min               0.0110465   0.0019779    5.58 0.0000000233875661 ***
timbre_4_max               0.0064668   0.0015413    4.20 0.0000272139788370 ***
timbre_5_min              -0.0051345   0.0012690   -4.05 0.0000520513667576 ***
timbre_5_max               0.0002979   0.0007856    0.38            0.70453    
timbre_6_min              -0.0178447   0.0022460   -7.94 0.0000000000000019 ***
timbre_6_max               0.0034469   0.0021821    1.58            0.11420    
timbre_7_min              -0.0051284   0.0017685   -2.90            0.00373 ** 
timbre_7_max              -0.0033935   0.0018198   -1.86            0.06221 .  
timbre_8_min               0.0036861   0.0028331    1.30            0.19323    
timbre_8_max               0.0046578   0.0029879    1.56            0.11902    
timbre_9_min              -0.0000932   0.0029569   -0.03            0.97486    
timbre_9_max               0.0013417   0.0024239    0.55            0.57990    
timbre_10_min              0.0040500   0.0018270    2.22            0.02664 *  
timbre_10_max              0.0057925   0.0017586    3.29            0.00099 ***
timbre_11_min             -0.0263767   0.0036829   -7.16 0.0000000000007958 ***
timbre_11_max              0.0198361   0.0033646    5.90 0.0000000037350899 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 6017.5  on 7200  degrees of freedom
Residual deviance: 4782.7  on 7168  degrees of freedom
AIC: 4849

Number of Fisher Scoring iterations: 6

# Yes

4 驗證模型 Validating Our Model

【4.1 正確性】What is the accuracy of Model 3 on the test set, using a threshold of 0.45?

PredTest = predict(SongsLog3,type="response",newdata = SongsTest)
table(SongsTest$Top10 , PredTest>0.45)

   
    FALSE TRUE
  0   309    5
  1    40   19

(309+19)/(309+5+40+19)

[1] 0.8794

【4.2 底線正確率】What would the accuracy of the baseline model be on the test set? ?

table(SongsTest$Top10)


  0   1 
314  59

314/(314+59)

[1] 0.8418

【4.3 正確性 vs. 辨識率】How many songs does Model 3 correctly predict as Top 10 hits in 2010? How many non-hit songs does Model 3 predict will be Top 10 hits?

table(SongsTest$Top10)


  0   1 
314  59

【Q】不能大幅度增加正確性的模型也會有用嗎？為甚麼？

事實上，正確性不適用於檢視模型最好的指標。由於根據樣本的不同，原先的樣本可能已經傾向於某種結果(baseline本來就很高)，於是利用logit模型可能也無法大幅提升正確性，並不能表示模型不佳。

【4.4 敏感性 & 明確性】What is the sensitivity and specificity of Model 3 on the test set, using a threshold of 0.45?

19/59

[1] 0.322

309/314

[1] 0.9841

【4.5 結論】What conclusions can you make about our model?


# Model 3 favors specificity over sensitivity.
# Model 3 provides conservative predictions, and predicts that a song will make it to the Top 10 very rarely. So while it detects less than half of the Top 10 songs, we can be very confident in the songs that it does predict to be Top 10 hits.

【Q】從這個結論我們學到什麼？

這題要預測歌曲是否能成為TOP10的歌曲，我認為在 sensitivity & specificity 的 trade-off 中偏向於提高sensitivity為主，因此當預測結論只有不到一半的歌曲能進入前10首歌曲中，是將閾值提高來提高sensitivity，因此對它預測的十大熱門歌曲非常有信心。單純從ACC來看，準確率極高(0.87)，看似MODEL很好，但是再看敏感性，卻是低的(0.32)。所以一個模型的好壞，應該不能只單看ACC。最好的話，我們希望兩者都很高。

---
title: "AS3-1 Group-4 Popularity of music records"
author: "王欣 M064111039"
output: html_notebook
---

```{r echo=T, message=F, cache=F, warning=F}
rm(list=ls(all=T))
options(digits=4, scipen=12)
library(dplyr); library(ggplot2)
```

- - -

### Introduction

議題：使用歌曲的屬性，預測它會不會進入流行歌曲排行榜的前10名

學習重點：

+ 依時間分割資料
+ model formula 的寫法
+ 高相關(共線性)自變數之間的選擇
+ accuracy, sensitivity, specificity的實際意義 
+ 如何調整臨界機率來權衡：TFR/sensitivity vs. FPR/specificity 

<br>

- - -

### 1 基本的資料處理 Understanding the Data

【**1.1**】How many observations (songs) are from the year 2010?
```{r}
setwd("C:/MIT summer 2018/Unit3/data")
Song = read.csv("songs.csv")
sum(Song$year==2010)
```

【**1.2**】How many songs does the dataset include for which the artist name is "Michael Jackson"?
```{r}
sum(Song$artistname=="Michael Jackson")
```

【**1.3**】Which of these songs by Michael Jackson made it to the Top 10? Select all that apply.
```{r}

A = subset(Song,artistname=="Michael Jackson")
A$songtitle[which(A$Top10==1)]
#You Rock My World , You Are Not Alone
```

【**1.4**】(a) What are the values of `timesignature` that occur in our dataset? (b) Which timesignature value is the most frequent among songs in our dataset? 
```{r}

summary(Song$timesignature)
table(Song$timesignature)
#0    1    3    4    5    7 
#4
```

【**1.5**】 Which of the following songs has the highest tempo?
```{r}
Song$songtitle[which.max(Song$tempo)]
```
<br>

- - -

### 2 建立模型 Creating Our Prediction Model

【**2.1 依時間分割資料**】How many observations (songs) are in the training set?
```{r}
SongsTrain = subset(Song,year<="2009")
SongsTest = subset(Song,year>"2009")
nrow(SongsTrain)
```

【**2.2 建立模型、模型摘要**】What is the value of the Akaike Information Criterion (AIC)?
```{r}
nonvars = c("year", "songtitle", "artistname", "songID", "artistID")
SongsTrain = SongsTrain[ , !(names(SongsTrain) %in% nonvars) ]

SongsTest = SongsTest[ ,!(names(SongsTest) %in% nonvars) ]
SongsLog1 = glm(Top10~.,data =SongsTrain,family = binomial)
summary(SongsLog1)
```

【**2.3 模型係數判讀**】The `LOWER` or `HIGHER` our confidence about time signature, key and tempo, the more likely the song is to be in the Top 10
```{r}

# time signature -> HIGHER
# key -> HIGHER
# tempo -> HIGHER
```

【**2.4 進行推論**】What does Model 1 suggest in terms of complexity?
```{r}

# Mainstream listeners tend to prefer less complex songs
```

【**2.5 檢查異常係數**】 (a) By inspecting the coefficient of the variable "loudness", what does Model 1 suggest? (b) By inspecting the coefficient of the variable "energy", do we draw the same conclusions as above?
```{r}

# Mainstream listeners prefer songs with heavy instrumentation
# No
```
<br>

- - -

### 3 處理共線性 Beware of Multicollinearity Issues!

【**3.1 檢查相關係數**】What is the correlation between `loudness` and `energy` in the training set?
```{r}
cor(SongsTrain$loudness,SongsTrain$energy)
```

【**3.2 重新建立模型、檢查係數**】Look at the summary of SongsLog2, and inspect the coefficient of the variable "energy". What do you observe?
```{r}

SongsLog2 = glm(Top10 ~ . - loudness, data=SongsTrain, family=binomial)
summary(SongsLog2)
# Model 2 suggests that songs with high energy levels tend to be more popular. This contradicts our observation in Model 1.
```

【**3.3 選擇模型**】 do we make the same observation about the popularity of heavy instrumentation as we did with Model 2?
```{r}

SongsLog3 = glm(Top10 ~ . - energy, data=SongsTrain, family=binomial)
summary(SongsLog3)
# Yes
```
<br>

- - -

### 4 驗證模型 Validating Our Model

【**4.1 正確性**】What is the accuracy of Model 3 on the test set, using a threshold of 0.45? 
```{r}
PredTest = predict(SongsLog3,type="response",newdata = SongsTest)
table(SongsTest$Top10 , PredTest>0.45)
(309+19)/(309+5+40+19)
```

【**4.2 底線正確率**】What would the accuracy of the baseline model be on the test set? ? 
```{r}
table(SongsTest$Top10)
314/(314+59)
```

【**4.3 正確性 vs. 辨識率**】How many songs does Model 3 correctly predict as Top 10 hits in 2010?  How many non-hit songs does Model 3 predict will be Top 10 hits?
```{r}

table(SongsTest$Top10)
#19
#5
```

【**Q**】不能大幅度增加正確性的模型也會有用嗎？為甚麼？

事實上，正確性不適用於檢視模型最好的指標。由於根據樣本的不同，原先的樣本可能已經傾向於某種結果(baseline本來就很高)，於是利用logit模型可能也無法大幅提升正確性，並不能表示模型不佳。

【**4.4 敏感性 & 明確性**】What is the `sensitivity` and `specificity` of Model 3 on the test set, using a threshold of 0.45?
```{r}
19/59
309/314
```

【**4.5 結論**】What conclusions can you make about our model?
```{r}

# Model 3 favors specificity over sensitivity.
# Model 3 provides conservative predictions, and predicts that a song will make it to the Top 10 very rarely. So while it detects less than half of the Top 10 songs, we can be very confident in the songs that it does predict to be Top 10 hits.
```

<br>

【**Q**】從這個結論我們學到什麼？

這題要預測歌曲是否能成為TOP10的歌曲，我認為在 sensitivity & specificity 的 trade-off 中偏向於提高sensitivity為主，因此當預測結論只有不到一半的歌曲能進入前10首歌曲中，是將閾值提高來提高sensitivity，因此對它預測的十大熱門歌曲非常有信心。
單純從ACC來看，準確率極高(0.87)，看似MODEL很好，但是再看敏感性，卻是低的(0.32)。所以一個模型的好壞，應該不能只單看ACC。最好的話，我們希望兩者都很高。


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AS3-1 Group-4 Popularity of music records

王欣 M064111039

Introduction

1 基本的資料處理 Understanding the Data

2 建立模型 Creating Our Prediction Model

3 處理共線性 Beware of Multicollinearity Issues!

4 驗證模型 Validating Our Model