daviddoji/JuliaForDataAnalysis
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

update layout of all exercises

Browse Source
This commit is contained in:
Bogumił Kamiński
2022-10-14 13:43:12 +02:00
parent 38398729ce
commit 31d8428f6a
11 changed files with 1042 additions and 925 deletions
Download Patch File Download Diff File Expand all files Collapse all files

242
exercises/exercises13.md
View File

@@ -13,12 +13,47 @@ https://archive.ics.uci.edu/ml/machine-learning-databases/00615/MushroomDataset.
archive and extract primary_data.csv and secondary_data.csv files from it.
Save the files to disk.
<details>
<summary>Solution</summary>
```
using Downloads
import ZipFile
Downloads.download("https://archive.ics.uci.edu/ml/machine-learning-databases/00615/MushroomDataset.zip", "MushroomDataset.zip")
archive = ZipFile.Reader("MushroomDataset.zip")
idx = only(findall(x -> contains(x.name, "primary_data.csv"), archive.files))
open("primary_data.csv", "w") do io
write(io, read(archive.files[idx]))
end
idx = only(findall(x -> contains(x.name, "secondary_data.csv"), archive.files))
open("secondary_data.csv", "w") do io
write(io, read(archive.files[idx]))
end
close(archive)
```
</details>
### Exercise 2
Load primary_data.csv into the `primary` data frame.
Load secondary_data.csv into the `secondary` data frame.
Describe the contents of both data frames.
<details>
<summary>Solution</summary>
```
using CSV
using DataFrames
primary = CSV.read("primary_data.csv", DataFrame; delim=';')
secondary = CSV.read("secondary_data.csv", DataFrame; delim=';')
describe(primary)
describe(secondary)
```
</details>
### Exercise 3
Start with `primary` data. Note that columns starting from column 4 have
@@ -32,6 +67,25 @@ three columns just after `class` column in the `parsed_primary` data frame.
Check `renamecols` keyword argument of `select` to
avoid renaming of the produced columns.
<details>
<summary>Solution</summary>
```
parse_nominal(s::AbstractString) = split(strip(s, ['[', ']']), ", ")
parse_nominal(::Missing) = missing
parse_numeric(s::AbstractString) = parse.(Float64, split(strip(s, ['[', ']']), ", "))
parse_numeric(::Missing) = missing
idcols = ["family", "name", "class"]
numericcols = ["cap-diameter", "stem-height", "stem-width"]
parsed_primary = select(primary,
idcols,
numericcols .=> ByRow(parse_numeric),
Not([idcols; numericcols]) .=> ByRow(parse_nominal);
renamecols=false)
```
</details>
### Exercise 4
In `parsed_primary` data frame find all pairs of mushrooms (rows) that might be
@@ -49,119 +103,8 @@ Use the following rules:
For each found pair print to the screen the row number, family, name, and class.
### Exercise 5
Still using `parsed_primary` find what is the average probability of class being
`p` by `family`. Additionally add number of observations in each group. Sort
these results by the probability. Try using DataFramesMeta.jl to do this
exercise (this requirement is optional).
Store the result in `agg_primary` data frame.
### Exercise 6
Now using `agg_primary` data frame collapse it so that for each unique `pr_p`
it gives us a total number of rows that had this probability and a tuple
of mushroom family names.
Optionally: try to display the produced table so that the tuple containing the
list of families for each group is not cropped (this will require large
terminal).
### Exercise 7
From our preliminary analysis of `primary` data we see that `missing` value in
the primary data is non-informative, so in `secondary` data we should be
cautious when building a model if we allowed for missing data (in practice
if we were investigating some real mushroom we most likely would know its
characteristics).
Therefore as a first step drop in-place all columns in `secondary` data frame
that have missing values.
### Exercise 8
Create a logistic regression predicting `class` based on all remaining features
in the data frame. You might need to check the `Term` usage in StatsModels.jl
documentation.
You will notice that for `stem-color` and `habitat` columns you get strange
estimation results (large absolute values of estimated parameters and even
larger standard errors). Explain why this happens by analyzing frequency tables
of these variables against `class` column.
### Exercise 9
Add `class_p` column to `secondary` as a second column that will contain
predicted probability from the model created in exercise 8 of a given
observation having class `p`.
Print descriptive statistics of column `class_p` by `class`.
### Exercise 10
Plot FPR-TPR ROC curve for our model and compute associated AUC value.
# Solutions
<details>
<summary>Show!</summary>
### Exercise 1
Solution:
```
using Downloads
import ZipFile
Downloads.download("https://archive.ics.uci.edu/ml/machine-learning-databases/00615/MushroomDataset.zip", "MushroomDataset.zip")
archive = ZipFile.Reader("MushroomDataset.zip")
idx = only(findall(x -> contains(x.name, "primary_data.csv"), archive.files))
open("primary_data.csv", "w") do io
write(io, read(archive.files[idx]))
end
idx = only(findall(x -> contains(x.name, "secondary_data.csv"), archive.files))
open("secondary_data.csv", "w") do io
write(io, read(archive.files[idx]))
end
close(archive)
```
### Exercise 2
Solution:
```
using CSV
using DataFrames
primary = CSV.read("primary_data.csv", DataFrame; delim=';')
secondary = CSV.read("secondary_data.csv", DataFrame; delim=';')
describe(primary)
describe(secondary)
```
### Exercise 3
Solution:
```
parse_nominal(s::AbstractString) = split(strip(s, ['[', ']']), ", ")
parse_nominal(::Missing) = missing
parse_numeric(s::AbstractString) = parse.(Float64, split(strip(s, ['[', ']']), ", "))
parse_numeric(::Missing) = missing
idcols = ["family", "name", "class"]
numericcols = ["cap-diameter", "stem-height", "stem-width"]
parsed_primary = select(primary,
idcols,
numericcols .=> ByRow(parse_numeric),
Not([idcols; numericcols]) .=> ByRow(parse_nominal);
renamecols=false)
```
### Exercise 4
Solution:
<summary>Solution</summary>
```
function overlap_numeric(v1, v2)
@@ -200,9 +143,19 @@ end
Note that in this exercise using `eachrow` is not a problem
(although it is not type stable) because the data is small.
</details>
### Exercise 5
Solution:
Still using `parsed_primary` find what is the average probability of class being
`p` by `family`. Additionally add number of observations in each group. Sort
these results by the probability. Try using DataFramesMeta.jl to do this
exercise (this requirement is optional).
Store the result in `agg_primary` data frame.
<details>
<summary>Solution</summary>
```
using Statistics
@@ -214,17 +167,40 @@ agg_primary = @chain parsed_primary begin
end
```
</details>
### Exercise 6
Solution:
Now using `agg_primary` data frame collapse it so that for each unique `pr_p`
it gives us a total number of rows that had this probability and a tuple
of mushroom family names.
Optionally: try to display the produced table so that the tuple containing the
list of families for each group is not cropped (this will require large
terminal).
<details>
<summary>Solution</summary>
```
show(combine(groupby(agg_primary, :pr_p), :nrow => sum => :nrow, :family => Tuple => :families), truncate=140)
show(combine(groupby(agg_primary, :pr_p), :nrow => sum => :nrow, :family => Tuple => :families); truncate=140)
```
</details>
### Exercise 7
Solution:
From our preliminary analysis of `primary` data we see that `missing` value in
the primary data is non-informative, so in `secondary` data we should be
cautious when building a model if we allowed for missing data (in practice
if we were investigating some real mushroom we most likely would know its
characteristics).
Therefore as a first step drop in-place all columns in `secondary` data frame
that have missing values.
<details>
<summary>Solution</summary>
```
select!(secondary, [!any(ismissing, col) for col in eachcol(secondary)])
@@ -233,9 +209,21 @@ select!(secondary, [!any(ismissing, col) for col in eachcol(secondary)])
Note that we select based on actual contents of the columns and not by their
element type (column could allow for missing values but not have them).
</details>
### Exercise 8
Solution:
Create a logistic regression predicting `class` based on all remaining features
in the data frame. You might need to check the `Term` usage in StatsModels.jl
documentation.
You will notice that for `stem-color` and `habitat` columns you get strange
estimation results (large absolute values of estimated parameters and even
larger standard errors). Explain why this happens by analyzing frequency tables
of these variables against `class` column.
<details>
<summary>Solution</summary>
```
using GLM
@@ -247,12 +235,21 @@ freqtable(secondary, "stem-color", "class")
freqtable(secondary, "habitat", "class")
```
We can see that for cetrain levels of `stem-color` and `habitat` variables
We can see that for certain levels of `stem-color` and `habitat` variables
there is a perfect separation of classes.
</details>
### Exercise 9
Solution:
Add `class_p` column to `secondary` as a second column that will contain
predicted probability from the model created in exercise 8 of a given
observation having class `p`.
Print descriptive statistics of column `class_p` by `class`.
<details>
<summary>Solution</summary>
```
insertcols!(secondary, 2, :class_p => predict(model))
@@ -264,9 +261,14 @@ end
We can see that the model has some discriminatory power, but there
is still a significant overlap between classes.
</details>
### Exercise 10
Solution:
Plot FPR-TPR ROC curve for our model and compute associated AUC value.
<details>
<summary>Solution</summary>
```
using Plots