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R-icntay 3 years ago
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commit 6dded01820
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4-Classification/1-Introduction/solution/R/lesson_10-R.ipynb
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@ -103,8 +103,8 @@
"cell_type": "code",
"execution_count": null,
"source": [
"suppressWarnings(if (!require(\"pacman\"))install.packages(\"pacman\"))\n",
"\n",
"suppressWarnings(if (!require(\"pacman\"))install.packages(\"pacman\"))\r\n",
"\r\n",
"pacman::p_load(tidyverse, tidymodels, DataExplorer, themis, here)"
],
"outputs": [],
@ -138,12 +138,12 @@
"cell_type": "code",
"execution_count": null,
"source": [
"# Import data\n",
"df <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/4-Classification/data/cuisines.csv\")\n",
"\n",
"# View the first 5 rows\n",
"df %>% \n",
" slice_head(n = 5)\n"
"# Import data\r\n",
"df <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/4-Classification/data/cuisines.csv\")\r\n",
"\r\n",
"# View the first 5 rows\r\n",
"df %>% \r\n",
" slice_head(n = 5)\r\n"
],
"outputs": [],
"metadata": {
@ -218,7 +218,7 @@
"\r\n",
"Next, let's assign each cuisine into its individual tibble and find out how much data is available (rows, columns) per cuisine.\r\n",
"\r\n",
"> A tibble is a modern reimagining of the data frame, keeping what time has proven to be effective, and throwing out what is not.\r\n",
"> A [tibble](https://tibble.tidyverse.org/) is a modern data frame.\r\n",
"\r\n",
"<p >\r\n",
" <img src=\"../../images/dplyr_filter.jpg\"\r\n",
@ -305,24 +305,24 @@
"cell_type": "code",
"execution_count": null,
"source": [
"# Creates a functions that returns the top ingredients by class\n",
"\n",
"create_ingredient <- function(df){\n",
" \n",
" # Drop the id column which is the first colum\n",
" ingredient_df = df %>% select(-1) %>% \n",
" # Transpose data to a long format\n",
" pivot_longer(!cuisine, names_to = \"ingredients\", values_to = \"count\") %>% \n",
" # Find the top most ingredients for a particular cuisine\n",
" group_by(ingredients) %>% \n",
" summarise(n_instances = sum(count)) %>% \n",
" filter(n_instances != 0) %>% \n",
" # Arrange by descending order\n",
" arrange(desc(n_instances)) %>% \n",
" mutate(ingredients = factor(ingredients) %>% fct_inorder())\n",
" \n",
" \n",
" return(ingredient_df)\n",
"# Creates a functions that returns the top ingredients by class\r\n",
"\r\n",
"create_ingredient <- function(df){\r\n",
" \r\n",
" # Drop the id column which is the first colum\r\n",
" ingredient_df = df %>% select(-1) %>% \r\n",
" # Transpose data to a long format\r\n",
" pivot_longer(!cuisine, names_to = \"ingredients\", values_to = \"count\") %>% \r\n",
" # Find the top most ingredients for a particular cuisine\r\n",
" group_by(ingredients) %>% \r\n",
" summarise(n_instances = sum(count)) %>% \r\n",
" filter(n_instances != 0) %>% \r\n",
" # Arrange by descending order\r\n",
" arrange(desc(n_instances)) %>% \r\n",
" mutate(ingredients = factor(ingredients) %>% fct_inorder())\r\n",
" \r\n",
" \r\n",
" return(ingredient_df)\r\n",
"} # End of function"
],
"outputs": [],
@ -343,10 +343,10 @@
"cell_type": "code",
"execution_count": null,
"source": [
"# Call create_ingredient and display popular ingredients\n",
"thai_ingredient_df <- create_ingredient(df = thai_df)\n",
"\n",
"thai_ingredient_df %>% \n",
"# Call create_ingredient and display popular ingredients\r\n",
"thai_ingredient_df <- create_ingredient(df = thai_df)\r\n",
"\r\n",
"thai_ingredient_df %>% \r\n",
" slice_head(n = 10)"
],
"outputs": [],
@ -367,11 +367,11 @@
"cell_type": "code",
"execution_count": null,
"source": [
"# Make a bar chart for popular thai cuisines\n",
"thai_ingredient_df %>% \n",
" slice_head(n = 10) %>% \n",
" ggplot(aes(x = n_instances, y = ingredients)) +\n",
" geom_bar(stat = \"identity\", width = 0.5, fill = \"steelblue\") +\n",
"# Make a bar chart for popular thai cuisines\r\n",
"thai_ingredient_df %>% \r\n",
" slice_head(n = 10) %>% \r\n",
" ggplot(aes(x = n_instances, y = ingredients)) +\r\n",
" geom_bar(stat = \"identity\", width = 0.5, fill = \"steelblue\") +\r\n",
" xlab(\"\") + ylab(\"\")"
],
"outputs": [],
@ -392,12 +392,12 @@
"cell_type": "code",
"execution_count": null,
"source": [
"# Get popular ingredients for Japanese cuisines and make bar chart\n",
"create_ingredient(df = japanese_df) %>% \n",
" slice_head(n = 10) %>%\n",
" ggplot(aes(x = n_instances, y = ingredients)) +\n",
" geom_bar(stat = \"identity\", width = 0.5, fill = \"darkorange\", alpha = 0.8) +\n",
" xlab(\"\") + ylab(\"\")\n"
"# Get popular ingredients for Japanese cuisines and make bar chart\r\n",
"create_ingredient(df = japanese_df) %>% \r\n",
" slice_head(n = 10) %>%\r\n",
" ggplot(aes(x = n_instances, y = ingredients)) +\r\n",
" geom_bar(stat = \"identity\", width = 0.5, fill = \"darkorange\", alpha = 0.8) +\r\n",
" xlab(\"\") + ylab(\"\")\r\n"
],
"outputs": [],
"metadata": {
@ -417,11 +417,11 @@
"cell_type": "code",
"execution_count": null,
"source": [
"# Get popular ingredients for Chinese cuisines and make bar chart\n",
"create_ingredient(df = chinese_df) %>% \n",
" slice_head(n = 10) %>%\n",
" ggplot(aes(x = n_instances, y = ingredients)) +\n",
" geom_bar(stat = \"identity\", width = 0.5, fill = \"cyan4\", alpha = 0.8) +\n",
"# Get popular ingredients for Chinese cuisines and make bar chart\r\n",
"create_ingredient(df = chinese_df) %>% \r\n",
" slice_head(n = 10) %>%\r\n",
" ggplot(aes(x = n_instances, y = ingredients)) +\r\n",
" geom_bar(stat = \"identity\", width = 0.5, fill = \"cyan4\", alpha = 0.8) +\r\n",
" xlab(\"\") + ylab(\"\")"
],
"outputs": [],
@ -442,11 +442,11 @@
"cell_type": "code",
"execution_count": null,
"source": [
"# Get popular ingredients for Indian cuisines and make bar chart\n",
"create_ingredient(df = indian_df) %>% \n",
" slice_head(n = 10) %>%\n",
" ggplot(aes(x = n_instances, y = ingredients)) +\n",
" geom_bar(stat = \"identity\", width = 0.5, fill = \"#041E42FF\", alpha = 0.8) +\n",
"# Get popular ingredients for Indian cuisines and make bar chart\r\n",
"create_ingredient(df = indian_df) %>% \r\n",
" slice_head(n = 10) %>%\r\n",
" ggplot(aes(x = n_instances, y = ingredients)) +\r\n",
" geom_bar(stat = \"identity\", width = 0.5, fill = \"#041E42FF\", alpha = 0.8) +\r\n",
" xlab(\"\") + ylab(\"\")"
],
"outputs": [],
@ -467,11 +467,11 @@
"cell_type": "code",
"execution_count": null,
"source": [
"# Get popular ingredients for Korean cuisines and make bar chart\n",
"create_ingredient(df = korean_df) %>% \n",
" slice_head(n = 10) %>%\n",
" ggplot(aes(x = n_instances, y = ingredients)) +\n",
" geom_bar(stat = \"identity\", width = 0.5, fill = \"#852419FF\", alpha = 0.8) +\n",
"# Get popular ingredients for Korean cuisines and make bar chart\r\n",
"create_ingredient(df = korean_df) %>% \r\n",
" slice_head(n = 10) %>%\r\n",
" ggplot(aes(x = n_instances, y = ingredients)) +\r\n",
" geom_bar(stat = \"identity\", width = 0.5, fill = \"#852419FF\", alpha = 0.8) +\r\n",
" xlab(\"\") + ylab(\"\")"
],
"outputs": [],
@ -494,12 +494,12 @@
"cell_type": "code",
"execution_count": null,
"source": [
"# Drop id column, rice, garlic and ginger from our original data set\n",
"df_select <- df %>% \n",
" select(-c(1, rice, garlic, ginger))\n",
"\n",
"# Display new data set\n",
"df_select %>% \n",
"# Drop id column, rice, garlic and ginger from our original data set\r\n",
"df_select <- df %>% \r\n",
" select(-c(1, rice, garlic, ginger))\r\n",
"\r\n",
"# Display new data set\r\n",
"df_select %>% \r\n",
" slice_head(n = 5)"
],
"outputs": [],
@ -510,16 +510,16 @@
{
"cell_type": "markdown",
"source": [
"## Preprocessing data using recipes 👩‍🍳👨‍🍳 - Dealing with imbalanced data ⚖️\n",
"\n",
"<p >\n",
" <img src=\"../../images/recipes.png\"\n",
" width=\"600\"/>\n",
" <figcaption>Artwork by @allison_horst</figcaption>\n",
"\n",
"Given that this lesson is about cuisines, we have to put `recipes` into context .\n",
"\n",
"Tidymodels provides yet another neat package: `recipes`- a package for preprocessing data.\n"
"## Preprocessing data using recipes 👩‍🍳👨‍🍳 - Dealing with imbalanced data ⚖️\r\n",
"\r\n",
"<p >\r\n",
" <img src=\"../../images/recipes.png\"\r\n",
" width=\"600\"/>\r\n",
" <figcaption>Artwork by @allison_horst</figcaption>\r\n",
"\r\n",
"Given that this lesson is about cuisines, we have to put `recipes` into context .\r\n",
"\r\n",
"Tidymodels provides yet another neat package: `recipes`- a package for preprocessing data.\r\n"
],
"metadata": {
"id": "kkFd-JxdIaL6"
@ -538,11 +538,11 @@
"cell_type": "code",
"execution_count": null,
"source": [
"# Distribution of cuisines\n",
"old_label_count <- df_select %>% \n",
" count(cuisine) %>% \n",
" arrange(desc(n))\n",
"\n",
"# Distribution of cuisines\r\n",
"old_label_count <- df_select %>% \r\n",
" count(cuisine) %>% \r\n",
" arrange(desc(n))\r\n",
"\r\n",
"old_label_count"
],
"outputs": [],
@ -572,13 +572,13 @@
"cell_type": "code",
"execution_count": null,
"source": [
"# Load themis package for dealing with imbalanced data\n",
"library(themis)\n",
"\n",
"# Create a recipe for preprocessing data\n",
"cuisines_recipe <- recipe(cuisine ~ ., data = df_select) %>% \n",
" step_smote(cuisine)\n",
"\n",
"# Load themis package for dealing with imbalanced data\r\n",
"library(themis)\r\n",
"\r\n",
"# Create a recipe for preprocessing data\r\n",
"cuisines_recipe <- recipe(cuisine ~ ., data = df_select) %>% \r\n",
" step_smote(cuisine)\r\n",
"\r\n",
"cuisines_recipe"
],
"outputs": [],
@ -609,18 +609,18 @@
"cell_type": "code",
"execution_count": null,
"source": [
"# Prep and bake the recipe\n",
"preprocessed_df <- cuisines_recipe %>% \n",
" prep() %>% \n",
" bake(new_data = NULL) %>% \n",
" relocate(cuisine)\n",
"\n",
"# Display data\n",
"preprocessed_df %>% \n",
" slice_head(n = 5)\n",
"\n",
"# Quick summary stats\n",
"preprocessed_df %>% \n",
"# Prep and bake the recipe\r\n",
"preprocessed_df <- cuisines_recipe %>% \r\n",
" prep() %>% \r\n",
" bake(new_data = NULL) %>% \r\n",
" relocate(cuisine)\r\n",
"\r\n",
"# Display data\r\n",
"preprocessed_df %>% \r\n",
" slice_head(n = 5)\r\n",
"\r\n",
"# Quick summary stats\r\n",
"preprocessed_df %>% \r\n",
" introduce()"
],
"outputs": [],
@ -641,12 +641,12 @@
"cell_type": "code",
"execution_count": null,
"source": [
"# Distribution of cuisines\n",
"new_label_count <- preprocessed_df %>% \n",
" count(cuisine) %>% \n",
" arrange(desc(n))\n",
"\n",
"list(new_label_count = new_label_count,\n",
"# Distribution of cuisines\r\n",
"new_label_count <- preprocessed_df %>% \r\n",
" count(cuisine) %>% \r\n",
" arrange(desc(n))\r\n",
"\r\n",
"list(new_label_count = new_label_count,\r\n",
" old_label_count = old_label_count)"
],
"outputs": [],
@ -675,7 +675,7 @@
"cell_type": "code",
"execution_count": null,
"source": [
"# Save preprocessed data\n",
"# Save preprocessed data\r\n",
"write_csv(preprocessed_df, \"../../../data/cleaned_cuisines_R.csv\")"
],
"outputs": [],
@ -686,32 +686,32 @@
{
"cell_type": "markdown",
"source": [
"This fresh CSV can now be found in the root data folder.\n",
"\n",
"**🚀Challenge**\n",
"\n",
"This curriculum contains several interesting datasets. Dig through the `data` folders and see if any contain datasets that would be appropriate for binary or multi-class classification? What questions would you ask of this dataset?\n",
"\n",
"## [**Post-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/20/)\n",
"\n",
"## **Review & Self Study**\n",
"\n",
"- Check out [package themis](https://github.com/tidymodels/themis). What other techniques could we use to deal with imbalanced data?\n",
"\n",
"- Tidy models [reference website](https://www.tidymodels.org/start/).\n",
"\n",
"- H. Wickham and G. Grolemund, [*R for Data Science: Visualize, Model, Transform, Tidy, and Import Data*](https://r4ds.had.co.nz/).\n",
"\n",
"#### THANK YOU TO:\n",
"\n",
"[`Allison Horst`](https://twitter.com/allison_horst/) for creating the amazing illustrations that make R more welcoming and engaging. Find more illustrations at her [gallery](https://www.google.com/url?q=https://github.com/allisonhorst/stats-illustrations&sa=D&source=editors&ust=1626380772530000&usg=AOvVaw3zcfyCizFQZpkSLzxiiQEM).\n",
"\n",
"[Cassie Breviu](https://www.twitter.com/cassieview) and [Jen Looper](https://www.twitter.com/jenlooper) for creating the original Python version of this module ♥️\n",
"\n",
"<p >\n",
" <img src=\"../../images/r_learners_sm.jpeg\"\n",
" width=\"600\"/>\n",
" <figcaption>Artwork by @allison_horst</figcaption>\n"
"This fresh CSV can now be found in the root data folder.\r\n",
"\r\n",
"**🚀Challenge**\r\n",
"\r\n",
"This curriculum contains several interesting datasets. Dig through the `data` folders and see if any contain datasets that would be appropriate for binary or multi-class classification? What questions would you ask of this dataset?\r\n",
"\r\n",
"## [**Post-lecture quiz**](https://white-water-09ec41f0f.azurestaticapps.net/quiz/20/)\r\n",
"\r\n",
"## **Review & Self Study**\r\n",
"\r\n",
"- Check out [package themis](https://github.com/tidymodels/themis). What other techniques could we use to deal with imbalanced data?\r\n",
"\r\n",
"- Tidy models [reference website](https://www.tidymodels.org/start/).\r\n",
"\r\n",
"- H. Wickham and G. Grolemund, [*R for Data Science: Visualize, Model, Transform, Tidy, and Import Data*](https://r4ds.had.co.nz/).\r\n",
"\r\n",
"#### THANK YOU TO:\r\n",
"\r\n",
"[`Allison Horst`](https://twitter.com/allison_horst/) for creating the amazing illustrations that make R more welcoming and engaging. Find more illustrations at her [gallery](https://www.google.com/url?q=https://github.com/allisonhorst/stats-illustrations&sa=D&source=editors&ust=1626380772530000&usg=AOvVaw3zcfyCizFQZpkSLzxiiQEM).\r\n",
"\r\n",
"[Cassie Breviu](https://www.twitter.com/cassieview) and [Jen Looper](https://www.twitter.com/jenlooper) for creating the original Python version of this module ♥️\r\n",
"\r\n",
"<p >\r\n",
" <img src=\"../../images/r_learners_sm.jpeg\"\r\n",
" width=\"600\"/>\r\n",
" <figcaption>Artwork by @allison_horst</figcaption>\r\n"
],
"metadata": {
"id": "WQs5621pMGwf"

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