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categorical_variables_02x03.jl
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categorical_variables_02x03.jl
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### A Pluto.jl notebook ###
# v0.19.22
using Markdown
using InteractiveUtils
# ╔═╡ 0246f270-b5dc-11ed-31b1-57eff55d0c61
using DataFrames, CSV, MLJ, MLJDecisionTreeInterface, CategoricalArrays, BetaML
# ╔═╡ 0ae4c1ac-b139-41cb-9af4-6cbd209d5f5b
md"""
## Step 1: Read the data
"""
# ╔═╡ 8e560e35-5125-4c54-9dee-339d12588491
begin
X = CSV.read("data//home-data-for-ml-course//train.csv", missingstring="NA", DataFrame)
X_test = CSV.read("data//home-data-for-ml-course//test.csv", missingstring="NA", DataFrame)
end;
# ╔═╡ 4088970c-b4fe-4838-9082-c1ea4a138601
schema(X)|> DataFrame;
# ╔═╡ 1e45f8e1-d24c-4eb7-aee9-76446af785ae
describe(X);
# ╔═╡ c4b40c08-34e3-43dd-9cd9-c8f749c5da9c
md"""
## Step2 : Drop missing rows from target column
"""
# ╔═╡ 562c45ae-1536-4fc7-91c7-319662a44236
dropmissing!(X, :SalePrice);
# in this case there is no missing rows in SalePrice Column
# ╔═╡ cdbe92bc-3ce2-4ac4-b4f4-104de649203a
md"""
## Step 3: Seperate target from predictors
#### target column assigned to y and then column droped from predictors
"""
# ╔═╡ fc106c69-5a31-4f41-b1f8-f89f118c8310
y = X.SalePrice;
# ╔═╡ 5cd6b7ad-54df-4368-b070-4c1c5c3f1eb5
select!(X, Not(:SalePrice)); # drop column SalePrice in Place
# ╔═╡ a7b272d8-5938-4201-890c-e2b3df0f82d2
# since this reactive notebook same variable names cannot be used
# y, X = unpack(X, ==(:SalePrice))
# ╔═╡ 5876b257-9c74-4b24-ae6f-fac95726569f
md"""
## Step 4: Data Cleaning
"""
# ╔═╡ a28dea95-9993-4da9-8d7d-c17ce338bca2
cols_with_missing = names(X, any.(ismissing, eachcol(X))) # columns names that contain missing values
# ╔═╡ da485bba-b442-46a9-8015-9f969cf844a6
select!(X, Not(cols_with_missing)); # drop column with missing values
# ╔═╡ 75278211-7bb2-4bb1-b976-3b722eeb6c37
md"""
## check X_test for datatype mismatch
"""
# ╔═╡ 562eec95-3b0f-403c-8ca4-2d0a1310a7fc
select!(X_test, Not(cols_with_missing)); # drop column with missing values
# ╔═╡ fe475b62-0bed-430f-8a05-4a912af3f76d
size(X), size(X_test)
# ╔═╡ 5e34a25b-c0c8-4439-b997-125cbf8b8e31
md"""
## Break off validation set from training data
"""
# ╔═╡ 3dd27220-35c6-4cff-a45c-44ec43998ba1
(X_train, X_valid), (y_train, y_valid) = MLJ.partition((X, y), 0.8, rng=0, multi=true);
# ╔═╡ 5ec978ff-1c51-46f6-921d-3c2f2e8da047
first(X_train, 5) # X_train[1:5,:]
# ╔═╡ a836f6af-b1e4-4cce-9d23-3209efc6b967
Forest = @load RandomForestRegressor pkg=DecisionTree verbosity=0
# ╔═╡ 6d89b8de-9cd1-4465-bc39-6aa0218776e5
function score_dataset(X_train, X_valid, y_train, y_valid)
model = Forest(n_trees=100, rng=0)
mach = machine(model, X_train, y_train, scitype_check_level=0) |> MLJ.fit!
preds = MLJ.predict(mach, X_valid)
return mean_absolute_error(preds, y_valid)
end
# ╔═╡ a8c9339c-46cd-4fd2-808b-2db9425fe151
md"""
### Drop columns with categorical data
in case of julia we droped column containing String Data for equivalent result in Tutorial
"""
# ╔═╡ 42bbe56a-dda3-4662-a305-77ab47938312
drop_X_train = select(X_train, Not(Cols(x -> eltype(X_train[:,x]) <: AbstractString)));
# ╔═╡ 0af40da0-54d5-4688-807e-cac37bc46987
names(drop_X_train) # checking Column names in drop_X_train
# ╔═╡ d11cdc8b-5cb0-4b4a-a208-2c3a54fe142b
drop_X_valid = select(X_valid, Not(Cols(x -> eltype(X_valid[:,x]) <: AbstractString)));
# ╔═╡ 33cc6295-1998-432d-a289-9aba94b95e12
size(drop_X_train), size(drop_X_valid)
# ╔═╡ e1ba8e7e-f967-4b93-918f-13e50727bef1
begin
println("MAE from Approach 1 (Drop categorical variables):")
println(score_dataset(drop_X_train, drop_X_valid, y_train, y_valid))
end
# ╔═╡ b5189c69-81cf-4835-aad4-f9b4ee8eedbf
begin
println("Unique values in 'Condition2' column in training data:", unique(X_train.Condition2))
println("\nUnique values in 'Condition2' column in validation data:", unique(X_valid.Condition2))
end
# ╔═╡ ae5b8597-9b3b-4cc5-bef3-debd1c61c911
md"""
## converting data in categorical
"""
# ╔═╡ c8e24819-1fae-429b-8eeb-7dc781741b07
begin
coerce!(X_train, autotype(X_train))
coerce!(X_valid, autotype(X_valid))
end;
# ╔═╡ ae21231b-dfff-41e5-b816-423e53aaf067
schema(X_train) |> DataFrame
# ╔═╡ f35d0799-0491-41e0-9172-be3a9e46f409
describe(X_train)
# ╔═╡ 28c0897a-4f1e-48d1-bb30-3d2de9f84abb
schema(X_valid) |> DataFrame
# ╔═╡ 0f1e48e9-6673-48c3-8ccd-d07721c9b989
begin
# Categorical columns in the training data
object_cols = [col for col in names(X_train) if eltype(X_train[!, col]) <: CategoricalValue]
# Columns that can be safely ordinal encoded
good_label_cols = [col for col in object_cols if
issubset(Set(X_valid[!, col]), Set(X_train[!, col]))]
# Problematic columns that will be dropped from the dataset
bad_label_cols = (setdiff(object_cols, good_label_cols))
println("Categorical columns that will be ordinal encoded: ", good_label_cols)
println("\nCategorical columns that will be dropped from the dataset: ", bad_label_cols)
end
# ╔═╡ 6d871e4c-eb16-4943-939a-d081b9a3d0dc
size(object_cols), size(good_label_cols)
# ╔═╡ 4ab08df3-87f6-4bcb-bbef-f08bfd5e192d
# Drop categorical columns that will not be encoded
label_X_train = X_train[:, Not(bad_label_cols)];
# ╔═╡ f15ba9b6-0629-42b7-86cd-0cfdf466c02c
label_X_valid = X_valid[:,Not(bad_label_cols)];
# ╔═╡ c85ca4fe-989c-4f7c-869a-7096b25b56b6
# Apply ordinal encoder
ordinal_encoder = OrdinalEncoder() # using BetaML
# ╔═╡ 891ec3c9-647f-4ac6-96e8-63e4bb5eace1
for col in good_label_cols
label_X_train[!, col] = BetaML.fit!(ordinal_encoder, X_train[!, col])
label_X_valid[!, col] = BetaML.fit!(ordinal_encoder, X_valid[!, col])
end
# ╔═╡ 96acb7d6-10b6-465d-8a31-f536149446db
vcat(first(label_X_train, 5), first(label_X_valid, 5))
# ╔═╡ c35dbfc6-66b6-47a1-b995-f0e5395073d4
begin
println("MAE from Approach 2 (Ordinal Encoding):")
println(score_dataset(label_X_train, label_X_valid, y_train, y_valid))
end
# ╔═╡ b4699ad0-1698-4695-bdba-598b7ac02197
unique(X_train.Neighborhood) |> length
# ╔═╡ f26891ab-7722-4bc8-aa20-c0c3c58ef26d
unique(X_train.Street)
# ╔═╡ 854a80af-b7b0-41a2-9c75-fc613e908ef5
md"## One-hot encoding"
# ╔═╡ ce72749b-7ab5-45af-b95e-00bfa0359c68
# Columns that will be one-hot encoded
low_cardinality_cols = [col for col in object_cols if length(unique(X_train[:,col])) < 10]
# ╔═╡ 3bff7c77-f93d-485e-886a-53295664879e
# Columns that will be dropped from the dataset
high_cardinality_cols = (setdiff(object_cols, low_cardinality_cols))
# ╔═╡ 4e08b65e-f1fa-43e8-ace1-8fa9ed87c286
good_label_cols_1 = [col for col in low_cardinality_cols if
issubset(Set(X_valid[!, col]), Set(X_train[!, col]))]
# ╔═╡ eb6dd51b-edc4-4183-90c3-3cb162b8c214
println("Categorical columns that will be one-hot encoded:", low_cardinality_cols)
# ╔═╡ 4975e927-9cd8-452d-8269-34c4230df8b3
println("\nCategorical columns that will be dropped from the dataset:", high_cardinality_cols)
# ╔═╡ 172519c9-60d2-425f-812e-436920977915
OH_Encoder = MLJ.OneHotEncoder()
# ╔═╡ cebc7656-bfb1-4147-b25a-0ed98fa53685
# Apply one-hot encoder to each column with categorical data
machlow = MLJ.fit!(machine(OH_Encoder, X_train[:, low_cardinality_cols]), verbosity=0)
# ╔═╡ 74a6206e-c10a-4a5e-aae5-7162a97820ef
OH_cols_train = MLJ.transform(machlow, X_train[:,low_cardinality_cols])
# ╔═╡ 77d2ef21-6eda-4e8f-b89a-c340763d9fdb
md"""
begin
levels!(X_valid.Utilities, levels(X_train.Utilities))
levels!(X_valid.Condition1, levels(X_train.Condition1))
levels!(X_valid.Condition2, levels(X_train.Condition2))
levels!(X_valid.OverallCond, levels(X_train.OverallCond))
levels!(X_valid.RoofStyle, levels(X_train.RoofStyle))
levels!(X_valid.RoofMatl, levels(X_train.RoofMatl))
levels!(X_valid.ExterCond, levels(X_train.ExterCond))
levels!(X_valid.Foundation, levels(X_train.Foundation))
levels!(X_valid.Heating, levels(X_train.Heating))
levels!(X_valid.HeatingQC, levels(X_train.HeatingQC))
end
"""
# ╔═╡ 4d05b243-9c05-4f6a-bf53-54de938e4f55
OH_cols_valid = MLJ.transform(machlow, X_valid[:,low_cardinality_cols])
# ╔═╡ 60e978c2-07c4-4f71-b4c6-4b27cef07264
unique(X_train.Utilities), unique(X_valid.Utilities)
# ╔═╡ 75847092-838d-4dc0-a8a4-4bbc248c6f7e
X_train.HeatingQC[29]
# ╔═╡ 7a9668dd-eca6-4799-9eb2-7c40d243ec6c
X_valid.HeatingQC[29]
# ╔═╡ 4b540dbb-d45a-49be-9c46-880ff821f03f
# One-hot encoding removed index; put it back
OH_cols_train.Id = X_train.Id;
# ╔═╡ 1b467289-388b-4513-8ceb-643e28baeb05
OH_cols_valid.Id = X_valid.Id
# ╔═╡ 6453a198-5084-4304-90e9-cab5fbb7d8bb
# Remove categorical columns (will replace with one-hot encoding)
num_X_train = X_train.drop(object_cols, axis=1)
# ╔═╡ db7fe782-2cab-4f41-a31d-cff592584a70
num_X_valid = X_valid.drop(object_cols, axis=1)
# ╔═╡ c54a6239-b596-4a4c-b795-7bed252d721e
# Add one-hot encoded columns to numerical features
OH_X_train = concat([num_X_train, OH_cols_train], axis=1)
# ╔═╡ 2141ae31-f501-4db3-af22-bf8f6ba21d5c
OH_X_valid = concat([num_X_valid, OH_cols_valid], axis=1)
# ╔═╡ 00000000-0000-0000-0000-000000000001
PLUTO_PROJECT_TOML_CONTENTS = """
[deps]
BetaML = "024491cd-cc6b-443e-8034-08ea7eb7db2b"
CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"
DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
MLJ = "add582a8-e3ab-11e8-2d5e-e98b27df1bc7"
MLJDecisionTreeInterface = "c6f25543-311c-4c74-83dc-3ea6d1015661"
[compat]
BetaML = "~0.9.6"
CSV = "~0.10.9"
CategoricalArrays = "~0.10.7"
DataFrames = "~1.5.0"
MLJ = "~0.19.1"
MLJDecisionTreeInterface = "~0.4.0"
"""
# ╔═╡ 00000000-0000-0000-0000-000000000002
PLUTO_MANIFEST_TOML_CONTENTS = """
# This file is machine-generated - editing it directly is not advised
julia_version = "1.9.0-rc1"
manifest_format = "2.0"
project_hash = "0270c79b0e927ec57cfea74138cf1464b2312cb5"
[[deps.ARFFFiles]]
deps = ["CategoricalArrays", "Dates", "Parsers", "Tables"]
git-tree-sha1 = "e8c8e0a2be6eb4f56b1672e46004463033daa409"
uuid = "da404889-ca92-49ff-9e8b-0aa6b4d38dc8"
version = "1.4.1"
[[deps.AbstractFFTs]]
deps = ["LinearAlgebra"]
git-tree-sha1 = "16b6dbc4cf7caee4e1e75c49485ec67b667098a0"
uuid = "621f4979-c628-5d54-868e-fcf4e3e8185c"
version = "1.3.1"
weakdeps = ["ChainRulesCore"]
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