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-0,0 +1,1425 @@ +--- +title: "模型构建" +subtitle: 《区域水环境污染数据分析实践》
Data analysis practice of regional water environment pollution +author: 苏命、王为东
中国科学院大学资源与环境学院
中国科学院生态环境研究中心 +date: today +lang: zh +format: + revealjs: + theme: dark + slide-number: true + chalkboard: + buttons: true + preview-links: auto + lang: zh + toc: true + toc-depth: 1 + toc-title: 大纲 + logo: ./_extensions/inst/img/ucaslogo.png + css: ./_extensions/inst/css/revealjs.css + pointer: + key: "p" + color: "#32cd32" + pointerSize: 18 +revealjs-plugins: + - pointer +filters: + - d2 +knitr: + opts_chunk: + dev: "svg" + retina: 3 +execute: + freeze: auto + cache: true + echo: true + fig-width: 5 + fig-height: 6 +--- + +# tidymodels主要步骤 + +```{r} +#| echo: false +hexes <- function(..., size = 64) { + x <- c(...) + x <- sort(unique(x), decreasing = TRUE) + right <- (seq_along(x) - 1) * size + res <- glue::glue( + '![](hexes/.png){.absolute top=-20 right= width="" height=""}', + .open = "<", .close = ">" + ) + paste0(res, collapse = " ") +} + +knitr::opts_chunk$set( + digits = 3, + comment = "#>", + dev = 'svglite' +) + +# devtools::install_github("gadenbuie/countdown") +# library(countdown) +library(ggplot2) +theme_set(theme_bw()) +options(cli.width = 70, ggplot2.discrete.fill = c("#7e96d5", "#de6c4e")) + +train_color <- "#1a162d" +test_color <- "#cd4173" +data_color <- "#767381" +assess_color <- "#84cae1" +splits_pal <- c(data_color, train_color, test_color) + +``` + + + + + +## 何为tidymodels? {background-image="images/tm-org.png" background-size="80%"} + +```{r load-tm} +#| message: true +#| echo: true +#| warning: true +library(tidymodels) +``` + +## 整体思路 + +```{r diagram-split, echo = FALSE} +#| fig-align: "center" + +knitr::include_graphics("images/whole-game-split.jpg") +``` + +## 整体思路 + +```{r diagram-model-1, echo = FALSE} +#| fig-align: "center" + +knitr::include_graphics("images/whole-game-model-1.jpg") +``` + +:::notes +Stress that we are **not** fitting a model on the entire training set other than for illustrative purposes in deck 2. +::: + +## 整体思路 + +```{r diagram-model-n, echo = FALSE} +#| fig-align: "center" + +knitr::include_graphics("images/whole-game-model-n.jpg") +``` + +## 整体思路 + +```{r, echo = FALSE} +#| fig-align: "center" + +knitr::include_graphics("images/whole-game-resamples.jpg") +``` + +## 整体思路 + +```{r, echo = FALSE} +#| fig-align: "center" + +knitr::include_graphics("images/whole-game-select.jpg") +``` + +## 整体思路 + +```{r diagram-final-fit, echo = FALSE} +#| fig-align: "center" + +knitr::include_graphics("images/whole-game-final-fit.jpg") +``` + +## 整体思路 + +```{r diagram-final-performance, echo = FALSE} +#| fig-align: "center" + +knitr::include_graphics("images/whole-game-final-performance.jpg") +``` + +## 相关包的安装 + +```{r load-pkgs} +#| eval: false + +# Install the packages for the workshop +pkgs <- + c("bonsai", "doParallel", "embed", "finetune", "lightgbm", "lme4", + "plumber", "probably", "ranger", "rpart", "rpart.plot", "rules", + "splines2", "stacks", "text2vec", "textrecipes", "tidymodels", + "vetiver", "remotes") + +install.packages(pkgs) +``` + +. . . + +

+ + + + +## Data on Chicago taxi trips + +```{r taxi-print} +library(tidymodels) +taxi +``` + +## 数据分割与使用 + +对于机器学习，我们通常将数据分成训练集和测试集： + +. . . + +- 训练集用于估计模型参数。 +- 测试集用于独立评估模型性能。 + +. . . + +在训练过程中不要使用测试集。 + + +. . . + +```{r test-train-split} +#| echo: false +#| fig.width: 12 +#| fig.height: 3 +#| +set.seed(123) +library(forcats) +one_split <- slice(taxi, 1:30) %>% + initial_split() %>% + tidy() %>% + add_row(Row = 1:30, Data = "Original") %>% + mutate(Data = case_when( + Data == "Analysis" ~ "Training", + Data == "Assessment" ~ "Testing", + TRUE ~ Data + )) %>% + mutate(Data = factor(Data, levels = c("Original", "Training", "Testing"))) +all_split <- + ggplot(one_split, aes(x = Row, y = fct_rev(Data), fill = Data)) + + geom_tile(color = "white", + linewidth = 1) + + scale_fill_manual(values = splits_pal, guide = "none") + + theme_minimal() + + theme(axis.text.y = element_text(size = rel(2)), + axis.text.x = element_blank(), + legend.position = "top", + panel.grid = element_blank()) + + coord_equal(ratio = 1) + + labs(x = NULL, y = NULL) +all_split +``` + +## The initial split + +```{r taxi-split} +set.seed(123) +taxi_split <- initial_split(taxi) +taxi_split +``` + +## Accessing the data + +```{r taxi-train-test} +taxi_train <- training(taxi_split) +taxi_test <- testing(taxi_split) +``` + +## The training set + +```{r taxi-train} +taxi_train +``` + +## 练习 + +```{r taxi-split-prop} +set.seed(123) +taxi_split <- initial_split(taxi, prop = 0.8) +taxi_train <- training(taxi_split) +taxi_test <- testing(taxi_split) + +nrow(taxi_train) +nrow(taxi_test) +``` + +## Stratification + +Use `strata = tip` + +```{r taxi-split-prop-strata} +set.seed(123) +taxi_split <- initial_split(taxi, prop = 0.8, strata = tip) +taxi_split +``` + +## Stratification + +Stratification often helps, with very little downside + +```{r taxi-tip-pct-by-split, echo = FALSE} +bind_rows( + taxi_train %>% mutate(split = "train"), + taxi_test %>% mutate(split = "test") +) %>% + ggplot(aes(x = split, fill = tip)) + + geom_bar(position = "fill") +``` + +## 模型类型 + + +模型多种多样 + +- `lm` for linear model + +- `glm` for generalized linear model (e.g. logistic regression) + +- `glmnet` for regularized regression + +- `keras` for regression using TensorFlow + +- `stan` for Bayesian regression + +- `spark` for large data sets + + +## 指定模型 + +```{r} +#| echo: false +library(tidymodels) + +set.seed(123) + +taxi_split <- initial_split(taxi, prop = 0.8, strata = tip) +taxi_train <- training(taxi_split) +taxi_test <- testing(taxi_split) +``` + +```{r logistic-reg} +logistic_reg() +``` + + +:::notes +Models have default engines +::: + + + +## To specify a model + +```{r logistic-reg-glmnet} +logistic_reg() %>% + set_engine("glmnet") +``` + +. . . + + +```{r logistic-reg-stan} +logistic_reg() %>% + set_engine("stan") +``` + + +::: columns +::: {.column width="40%"} +- Choose a model +- Specify an engine +- Set the [mode]{.underline} +::: + +::: {.column width="60%"} +![](images/taxi_spinning.svg) +::: +::: + + +## To specify a model + +```{r decision-tree} +decision_tree() +``` + +:::notes +Some models have a default mode +::: + +## To specify a model + +```{r decision-tree-classification} +decision_tree() %>% + set_mode("classification") +``` + +. . . + +

+ +::: r-fit-text +All available models are listed at +::: + + +## Workflows + +```{r good-workflow} +#| echo: false +#| out-width: '70%' +#| fig-align: 'center' +knitr::include_graphics("images/good_workflow.png") +``` + + +## 为什么要使用 `workflow()`? + + +- 与基本的 R 工具相比，工作流能更好地处理新的因子水平 + +. . . + +- 除了公式之外，还可以使用其他的预处理器（更多关于高级 tidymodels 中的特征工程！） + +. . . + +- 在使用多个模型时，它们可以帮助组织工作 + +. . . + +- [最重要的是]{.underline}，工作流涵盖了整个建模过程：`fit()` 和 `predict()` 不仅适用于实际的模型拟合，还适用于预处理步骤 + +::: notes +工作流比基本的 R 处理水平更好的两种方式： + +- 强制要求在预测时不允许出现新的水平（这是一个可选的检查，可以关闭） + +- 恢复在拟合时存在但在预测时缺失的水平（例如，“新”数据中没有该水平的实例） +::: + + +## A model workflow + +```{r tree-spec} +tree_spec <- + decision_tree(cost_complexity = 0.002) %>% + set_mode("classification") + +tree_spec %>% + fit(tip ~ ., data = taxi_train) +``` + +## A model workflow + +```{r tree-wflow} +tree_spec <- + decision_tree(cost_complexity = 0.002) %>% + set_mode("classification") + +workflow() %>% + add_formula(tip ~ .) %>% + add_model(tree_spec) %>% + fit(data = taxi_train) +``` + +## A model workflow + +```{r tree-wflow-fit} +tree_spec <- + decision_tree(cost_complexity = 0.002) %>% + set_mode("classification") + +workflow(tip ~ ., tree_spec) %>% + fit(data = taxi_train) +``` + +## 预测 + +How do you use your new `tree_fit` model? + +```{r tree-wflow-fit-2} +tree_spec <- + decision_tree(cost_complexity = 0.002) %>% + set_mode("classification") + +tree_fit <- + workflow(tip ~ ., tree_spec) %>% + fit(data = taxi_train) +``` + +## 练习 + +*Run:* + +`predict(tree_fit, new_data = taxi_test)` + + + +. . . + + +*Run:* + +`augment(tree_fit, new_data = taxi_test)` + +*What do you get?* + + +## tidymodels 的预测 + +- 预测结果始终在一个 **tibble** 内 +- 列名和类型可读性强 +- `new_data` 中的行数和输出中的行数**相同** + +## 理解模型 + +如何 **理解**`tree_fit` 模型？ + +```{r plot-tree-fit-4} +#| echo: false +#| fig-align: center +#| fig-width: 8 +#| fig-height: 5 +#| out-width: 100% +library(rpart.plot) +tree_fit %>% + extract_fit_engine() %>% + rpart.plot(roundint = FALSE) +``` + +## Evaluating models: 预测值 + +```{r} +#| echo: false +library(tidymodels) + +set.seed(123) +taxi_split <- initial_split(taxi, prop = 0.8, strata = tip) +taxi_train <- training(taxi_split) +taxi_test <- testing(taxi_split) + +tree_spec <- decision_tree(cost_complexity = 0.0001, mode = "classification") +taxi_wflow <- workflow(tip ~ ., tree_spec) +taxi_fit <- fit(taxi_wflow, taxi_train) +``` + +```{r taxi-fit-augment} +augment(taxi_fit, new_data = taxi_train) %>% + relocate(tip, .pred_class, .pred_yes, .pred_no) +``` + +## Confusion matrix + +![](images/confusion-matrix.png) + +## Confusion matrix + +```{r conf-mat} +augment(taxi_fit, new_data = taxi_train) %>% + conf_mat(truth = tip, estimate = .pred_class) +``` + +## Confusion matrix + +```{r conf-mat-plot} +augment(taxi_fit, new_data = taxi_train) %>% + conf_mat(truth = tip, estimate = .pred_class) %>% + autoplot(type = "heatmap") +``` + +## Metrics for model performance + +::: columns +::: {.column width="60%"} +```{r acc} +augment(taxi_fit, new_data = taxi_train) %>% + accuracy(truth = tip, estimate = .pred_class) +``` +::: + +::: {.column width="40%"} +![](images/confusion-matrix-accuracy.png) +::: +::: + +## 二分类模型评估 + +模型的敏感性（Sensitivity）和特异性（Specificity）是评估二分类模型性能的重要指标： + +- **敏感性**（Sensitivity），也称为真阳性率，衡量了模型正确识别正类别样本的能力。公式为真阳性数除以真阳性数加上假阴性数： + +$$ +\text{Sensitivity} = \frac{\text{True Positives}}{\text{True Positives} + \text{False Negatives}} +$$ + + +- **特异性**（Specificity），也称为真阴性率，衡量了模型正确识别负类别样本的能力。公式为真阴性数除以真阴性数加上假阳性数： + +$$ +\text{Specificity} = \frac{\text{True Negatives}}{\text{True Negatives} + \text{False Positives}} +$$ + +在评估模型时，我们希望敏感性和特异性都很高。高敏感性表示模型能够捕获真正的正类别样本，高特异性表示模型能够准确排除负类别样本。 + + +## Metrics for model performance + +::: columns +::: {.column width="60%"} +```{r sens} +augment(taxi_fit, new_data = taxi_train) %>% + sensitivity(truth = tip, estimate = .pred_class) +``` +::: + +::: {.column width="40%"} +![](images/confusion-matrix-sensitivity.png) +::: +::: + + +## Metrics for model performance + +::: columns +::: {.column width="60%"} +```{r sens-2} +#| code-line-numbers: "3-6" +augment(taxi_fit, new_data = taxi_train) %>% + sensitivity(truth = tip, estimate = .pred_class) +``` + +
+ +```{r spec} +augment(taxi_fit, new_data = taxi_train) %>% + specificity(truth = tip, estimate = .pred_class) +``` +::: + +::: {.column width="40%"} +![](images/confusion-matrix-specificity.png) +::: +::: + +## Metrics for model performance + +We can use `metric_set()` to combine multiple calculations into one + +```{r taxi-metrics} +taxi_metrics <- metric_set(accuracy, specificity, sensitivity) + +augment(taxi_fit, new_data = taxi_train) %>% + taxi_metrics(truth = tip, estimate = .pred_class) +``` + +## Metrics for model performance + +```{r taxi-metrics-grouped} +taxi_metrics <- metric_set(accuracy, specificity, sensitivity) + +augment(taxi_fit, new_data = taxi_train) %>% + group_by(local) %>% + taxi_metrics(truth = tip, estimate = .pred_class) +``` + + +## Varying the threshold + +```{r} +#| label: thresholds +#| echo: false + +augment(taxi_fit, new_data = taxi_train) %>% + roc_curve(truth = tip, .pred_yes) %>% + filter(is.finite(.threshold)) %>% + pivot_longer(c(specificity, sensitivity), names_to = "statistic", values_to = "value") %>% + rename(`event threshold` = .threshold) %>% + ggplot(aes(x = `event threshold`, y = value, col = statistic, group = statistic)) + + geom_line() + + scale_color_brewer(palette = "Dark2") + + labs(y = NULL) + + coord_equal() + + theme(legend.position = "top") +``` + +## ROC 曲线 + +- ROC（Receiver Operating Characteristic）曲线用于评估二分类模型的性能，特别是在不同的阈值下比较模型的敏感性和特异性。 +- ROC曲线的横轴是假阳性率（False Positive Rate，FPR），纵轴是真阳性率（True Positive Rate，TPR）。在ROC曲线上，每个点对应于一个特定的阈值。通过改变阈值，我们可以观察到模型在不同条件下的表现。 +- ROC曲线越接近左上角（0,1）点，说明模型的性能越好，因为这表示在较低的假阳性率下，模型能够获得较高的真阳性率。ROC曲线下面积（Area Under the ROC Curve，AUC）也是评估模型性能的一种指标，AUC值越大表示模型性能越好。 + + + +## ROC curve plot + +```{r roc-curve} +#| fig-width: 6 +#| fig-height: 6 +#| output-location: "column" + +augment(taxi_fit, new_data = taxi_train) %>% + roc_curve(truth = tip, .pred_yes) %>% + autoplot() +``` + + +## 过度拟合 + +![](./images/tuning-overfitting-train-1.svg) + +## 过度拟合 + +![](images/tuning-overfitting-test-1.svg) + + +## Cross-validation {background-color="white" background-image="https://www.tmwr.org/premade/resampling.svg" background-size="80%"} + +## Cross-validation + +![](https://www.tmwr.org/premade/three-CV.svg) + +## Cross-validation + +![](https://www.tmwr.org/premade/three-CV-iter.svg) + +## Cross-validation + +```{r vfold-cv} +vfold_cv(taxi_train) # v = 10 is default +``` + +## Cross-validation + +What is in this? + +```{r taxi-splits} +taxi_folds <- vfold_cv(taxi_train) +taxi_folds$splits[1:3] +``` + +::: notes +Talk about a list column, storing non-atomic types in dataframe +::: + +## Cross-validation + +```{r vfold-cv-v} +vfold_cv(taxi_train, v = 5) +``` + +## Cross-validation + +```{r vfold-cv-strata} +vfold_cv(taxi_train, strata = tip) +``` + +. . . + +Stratification often helps, with very little downside + +## Cross-validation + +We'll use this setup: + +```{r taxi-folds} +set.seed(123) +taxi_folds <- vfold_cv(taxi_train, v = 10, strata = tip) +taxi_folds +``` + +. . . + +Set the seed when creating resamples + + +## Fit our model to the resamples + +```{r fit-resamples} +taxi_res <- fit_resamples(taxi_wflow, taxi_folds) +taxi_res +``` + +## Evaluating model performance + +```{r collect-metrics} +taxi_res %>% + collect_metrics() +``` + +::: notes +collect_metrics() 是一套 collect_*() 函数之一，可用于处理调参结果的列。调参结果中以 . 为前缀的大多数列都有对应的 collect_*() 函数，可以进行常见摘要选项的汇总。 +::: + +. . . + +We can reliably measure performance using only the **training** data 🎉 + +## Comparing metrics + +How do the metrics from resampling compare to the metrics from training and testing? + +```{r calc-roc-auc} +#| echo: false +taxi_training_roc_auc <- + taxi_fit %>% + augment(taxi_train) %>% + roc_auc(tip, .pred_yes) %>% + pull(.estimate) %>% + round(digits = 2) + +taxi_testing_roc_auc <- + taxi_fit %>% + augment(taxi_test) %>% + roc_auc(tip, .pred_yes) %>% + pull(.estimate) %>% + round(digits = 2) +``` + +::: columns +::: {.column width="50%"} +```{r collect-metrics-2} +taxi_res %>% + collect_metrics() %>% + select(.metric, mean, n) +``` +::: + +::: {.column width="50%"} +The ROC AUC previously was + +- `r taxi_training_roc_auc` for the training set +- `r taxi_testing_roc_auc` for test set +::: +::: + +. . . + +Remember that: + +⚠️ the training set gives you overly optimistic metrics + +⚠️ the test set is precious + +## Evaluating model performance + +```{r save-predictions} +# Save the assessment set results +ctrl_taxi <- control_resamples(save_pred = TRUE) +taxi_res <- fit_resamples(taxi_wflow, taxi_folds, control = ctrl_taxi) + +taxi_res +``` + +## Evaluating model performance + +```{r collect-predictions} +# Save the assessment set results +taxi_preds <- collect_predictions(taxi_res) +taxi_preds +``` + +## Evaluating model performance + +```{r taxi-metrics-by-id} +taxi_preds %>% + group_by(id) %>% + taxi_metrics(truth = tip, estimate = .pred_class) +``` + +## Where are the fitted models? + +```{r taxi-res} +taxi_res +``` + + +## Bootstrapping + +![](https://www.tmwr.org/premade/bootstraps.svg) + +## Bootstrapping + +```{r bootstraps} +set.seed(3214) +bootstraps(taxi_train) +``` + + +## Monte Carlo Cross-Validation + +```{r mc-cv} +set.seed(322) +mc_cv(taxi_train, times = 10) +``` + +## Validation set + +```{r validation-split} +set.seed(853) +taxi_val_split <- initial_validation_split(taxi, strata = tip) +validation_set(taxi_val_split) +``` + + +## Create a random forest model + +```{r rf-spec} +rf_spec <- rand_forest(trees = 1000, mode = "classification") +rf_spec +``` + +## Create a random forest model + +```{r rf-wflow} +rf_wflow <- workflow(tip ~ ., rf_spec) +rf_wflow +``` + +## Evaluating model performance + +```{r collect-metrics-rf} +ctrl_taxi <- control_resamples(save_pred = TRUE) + +# Random forest uses random numbers so set the seed first + +set.seed(2) +rf_res <- fit_resamples(rf_wflow, taxi_folds, control = ctrl_taxi) +collect_metrics(rf_res) +``` + +## The whole game - status update + +```{r diagram-select, echo = FALSE} +#| fig-align: "center" + +knitr::include_graphics("images/whole-game-transparent-select.jpg") +``` + +## The final fit + +```{r final-fit} +# taxi_split has train + test info +final_fit <- last_fit(rf_wflow, taxi_split) + +final_fit +``` + +## 何为`final_fit`? + +```{r collect-metrics-final-fit} +collect_metrics(final_fit) +``` + +. . . + +These are metrics computed with the **test** set + +## 何为`final_fit`? + +```{r collect-predictions-final-fit} +collect_predictions(final_fit) +``` + +## 何为`final_fit`? + +```{r extract-workflow} +extract_workflow(final_fit) +``` + +. . . + +Use this for **prediction** on new data, like for deploying + + + + +## Tuning models - Specifying tuning parameters + + +```{r} +#| label: tag-for-tuning +#| code-line-numbers: "1|" + +rf_spec <- rand_forest(min_n = tune()) %>% + set_mode("classification") + +rf_wflow <- workflow(tip ~ ., rf_spec) +rf_wflow +``` + +## Try out multiple values + +`tune_grid()` works similar to `fit_resamples()` but covers multiple parameter values: + +```{r} +#| label: rf-tune_grid +#| code-line-numbers: "2|3-4|5|" + +set.seed(22) +rf_res <- tune_grid( + rf_wflow, + taxi_folds, + grid = 5 +) +``` + +## Compare results + +Inspecting results and selecting the best-performing hyperparameter(s): + +```{r} +#| label: rf-results + +show_best(rf_res) + +best_parameter <- select_best(rf_res) +best_parameter +``` + +`collect_metrics()` and `autoplot()` are also available. + +## The final fit + +```{r} +#| label: rf-finalize + +rf_wflow <- finalize_workflow(rf_wflow, best_parameter) + +final_fit <- last_fit(rf_wflow, taxi_split) + +collect_metrics(final_fit) +``` + +# 实践部分 + + +## 数据 + +```{r} +require(tidyverse) +sitedf <- readr::read_csv("https://www.epa.gov/sites/default/files/2014-01/nla2007_sampledlakeinformation_20091113.csv") |> + select(SITE_ID, + lon = LON_DD, + lat = LAT_DD, + name = LAKENAME, + area = LAKEAREA, + zmax = DEPTHMAX + ) |> + group_by(SITE_ID) |> + summarize(lon = mean(lon, na.rm = TRUE), + lat = mean(lat, na.rm = TRUE), + name = unique(name), + area = mean(area, na.rm = TRUE), + zmax = mean(zmax, na.rm = TRUE)) + + +visitdf <- readr::read_csv("https://www.epa.gov/sites/default/files/2013-09/nla2007_profile_20091008.csv") |> + select(SITE_ID, + date = DATE_PROFILE, + year = YEAR, + visit = VISIT_NO + ) |> + distinct() + + + +waterchemdf <- readr::read_csv("https://www.epa.gov/sites/default/files/2013-09/nla2007_profile_20091008.csv") |> + select(SITE_ID, + date = DATE_PROFILE, + depth = DEPTH, + temp = TEMP_FIELD, + do = DO_FIELD, + ph = PH_FIELD, + cond = COND_FIELD, + ) + +sddf <- readr::read_csv("https://www.epa.gov/sites/default/files/2014-10/nla2007_secchi_20091008.csv") |> + select(SITE_ID, + date = DATE_SECCHI, + sd = SECMEAN, + clear_to_bottom = CLEAR_TO_BOTTOM + ) + +trophicdf <- readr::read_csv("https://www.epa.gov/sites/default/files/2014-10/nla2007_trophic_conditionestimate_20091123.csv") |> + select(SITE_ID, + visit = VISIT_NO, + tp = PTL, + tn = NTL, + chla = CHLA) |> + left_join(visitdf, by = c("SITE_ID", "visit")) |> + select(-year, -visit) |> + group_by(SITE_ID, date) |> + summarize(tp = mean(tp, na.rm = TRUE), + tn = mean(tn, na.rm = TRUE), + chla = mean(chla, na.rm = TRUE) + ) + + + +phytodf <- readr::read_csv("https://www.epa.gov/sites/default/files/2014-10/nla2007_phytoplankton_softalgaecount_20091023.csv") |> + select(SITE_ID, + date = DATEPHYT, + depth = SAMPLE_DEPTH, + phyta = DIVISION, + genus = GENUS, + species = SPECIES, + tax = TAXANAME, + abund = ABUND) |> + mutate(phyta = gsub(" .*$", "", phyta)) |> + filter(!is.na(genus)) |> + group_by(SITE_ID, date, depth, phyta, genus) |> + summarize(abund = sum(abund, na.rm = TRUE)) |> + nest(phytodf = -c(SITE_ID, date)) + +envdf <- waterchemdf |> + filter(depth < 2) |> + select(-depth) |> + group_by(SITE_ID, date) |> + summarise_all(~mean(., na.rm = TRUE)) |> + ungroup() |> + left_join(sddf, by = c("SITE_ID", "date")) |> + left_join(trophicdf, by = c("SITE_ID", "date")) + +nla <- envdf |> + left_join(phytodf) |> + left_join(sitedf, by = "SITE_ID") |> + filter(!purrr::map_lgl(phytodf, is.null)) |> + mutate(cyanophyta = purrr::map(phytodf, ~ .x |> + dplyr::filter(phyta == "Cyanophyta") |> + summarize(cyanophyta = sum(abund, na.rm = TRUE)) + )) |> + unnest(cyanophyta) |> + select(-phyta) |> + mutate(clear_to_bottom = ifelse(is.na(clear_to_bottom), TRUE, FALSE)) + + +# library(rmdify) +# library(dwfun) +# dwfun::init() + +``` + + +## 数据 + +```{r} +skimr::skim(nla) +``` + + + +## 简单模型 + +```{r} +nla |> + filter(tp > 1) |> + ggplot(aes(tn, tp)) + +geom_point() + +geom_smooth(method = "lm") + +scale_x_log10(breaks = scales::trans_breaks("log10", function(x) 10^x), + labels = scales::trans_format("log10", scales::math_format(10^.x))) + +scale_y_log10(breaks = scales::trans_breaks("log10", function(x) 10^x), + labels = scales::trans_format("log10", scales::math_format(10^.x))) + +m1 <- lm(log10(tp) ~ log10(tn), data = nla) + +summary(m1) + + +``` + +## 复杂指标 + +```{r} +nla |> + filter(tp > 1) |> + ggplot(aes(tp, cyanophyta)) + +geom_point() + +geom_smooth(method = "lm") + +scale_x_log10(breaks = scales::trans_breaks("log10", function(x) 10^x), + labels = scales::trans_format("log10", scales::math_format(10^.x))) + +scale_y_log10(breaks = scales::trans_breaks("log10", function(x) 10^x), + labels = scales::trans_format("log10", scales::math_format(10^.x))) + +m2 <- lm(log10(cyanophyta) ~ log10(tp), data = nla) + +summary(m2) + + +``` + + + + +## tidymodels - Data split + +```{r} +(nla_split <- rsample::initial_split(nla, prop = 0.7, strata = zmax)) +(nla_train <- training(nla_split)) +(nla_test <- testing(nla_split)) + +``` + +## tidymodels - recipe + +```{r} +nla_formula <- as.formula("cyanophyta ~ temp + do + ph + cond + sd + tp + tn + chla + clear_to_bottom") +# nla_formula <- as.formula("cyanophyta ~ temp + do + ph + cond + sd + tp + tn") +nla_recipe <- recipes::recipe(nla_formula, data = nla_train) |> + recipes::step_string2factor(all_nominal()) |> + recipes::step_nzv(all_nominal()) |> + recipes::step_log(chla, cyanophyta, base = 10) |> + recipes::step_normalize(all_numeric_predictors()) |> + prep() +nla_recipe +``` + +## tidymodels - cross validation + +```{r} +nla_cv <- recipes::bake( + nla_recipe, + new_data = training(nla_split) + ) |> + rsample::vfold_cv(v = 10) +nla_cv +``` + +## tidymodels - Model specification + +```{r} +xgboost_model <- parsnip::boost_tree( + mode = "regression", + trees = 1000, + min_n = tune(), + tree_depth = tune(), + learn_rate = tune(), + loss_reduction = tune() +) |> + set_engine("xgboost", objective = "reg:squarederror") +xgboost_model +``` + + +## tidymodels - Grid specification + +```{r} +# grid specification +xgboost_params <- dials::parameters( + min_n(), + tree_depth(), + learn_rate(), + loss_reduction() +) +xgboost_params +``` + +## tidymodels - Grid specification + +```{r} +xgboost_grid <- dials::grid_max_entropy( + xgboost_params, + size = 60 +) +knitr::kable(head(xgboost_grid)) +``` + +## tidymodels - Workflow + +```{r} +xgboost_wf <- workflows::workflow() |> + add_model(xgboost_model) |> + add_formula(nla_formula) +xgboost_wf +``` + + +## tidymodels - Tune + +```{r} +#| cache: true +# hyperparameter tuning +if (FALSE) { + xgboost_tuned <- tune::tune_grid( + object = xgboost_wf, + resamples = nla_cv, + grid = xgboost_grid, + metrics = yardstick::metric_set(rmse, rsq, mae), + control = tune::control_grid(verbose = TRUE) + ) +saveRDS(xgboost_tuned, "./xgboost_tuned.RDS") +} +xgboost_tuned <- readRDS("./xgboost_tuned.RDS") +``` + +## tidymodels - Best model + +```{r} +xgboost_tuned |> + tune::show_best(metric = "rmse") |> + knitr::kable() +``` + + +## tidymodels - Best model + +```{r} +xgboost_tuned |> + collect_metrics() +``` + + +## tidymodels - Best model + +```{r} +#| fig-width: 9 +#| fig-height: 5 +#| out-width: "100%" +xgboost_tuned |> + autoplot() +``` + + +## tidymodels - Best model + + +```{r} +xgboost_best_params <- xgboost_tuned |> + tune::select_best("rmse") + +knitr::kable(xgboost_best_params) +``` + + +## tidymodels - Final model + +```{r} +xgboost_model_final <- xgboost_model |> + finalize_model(xgboost_best_params) +xgboost_model_final +``` + + +## tidymodels - Train evaluation + + +```{r} +(train_processed <- bake(nla_recipe, new_data = nla_train)) +``` + +## tidymodels - Train data + +```{r} +train_prediction <- xgboost_model_final |> + # fit the model on all the training data + fit( + formula = nla_formula, + data = train_processed + ) |> + # predict the sale prices for the training data + predict(new_data = train_processed) |> + bind_cols(nla_train |> + mutate(.obs = log10(cyanophyta))) +xgboost_score_train <- + train_prediction |> + yardstick::metrics(.obs, .pred) |> + mutate(.estimate = format(round(.estimate, 2), big.mark = ",")) +knitr::kable(xgboost_score_train) + +``` + +## tidymodels - train evaluation + +```{r} +#| fig-width: 5 +#| fig-height: 3 +#| out-width: "80%" +train_prediction |> + ggplot(aes(.pred, .obs)) + +geom_point() + +geom_smooth(method = "lm") + + +``` + + +## tidymodels - test data + + +```{r} +test_processed <- bake(nla_recipe, new_data = nla_test) + +test_prediction <- xgboost_model_final |> + # fit the model on all the training data + fit( + formula = nla_formula, + data = train_processed + ) |> + # use the training model fit to predict the test data + predict(new_data = test_processed) |> + bind_cols(nla_test |> + mutate(.obs = log10(cyanophyta))) + +# measure the accuracy of our model using `yardstick` +xgboost_score <- test_prediction |> + yardstick::metrics(.obs, .pred) |> + mutate(.estimate = format(round(.estimate, 2), big.mark = ",")) + +knitr::kable(xgboost_score) +``` + + +## tidymodels - evaluation + +```{r} +#| fig-width: 5 +#| fig-height: 3 +#| out-width: "80%" +cyanophyta_prediction_residual <- test_prediction |> + arrange(.pred) %>% + mutate(residual_pct = (.obs - .pred) / .pred) |> + select(.pred, residual_pct) + +cyanophyta_prediction_residual |> +ggplot(aes(x = .pred, y = residual_pct)) + + geom_point() + + xlab("Predicted Cyanophyta") + + ylab("Residual (%)") +``` + + + + +## tidymodels - test evaluation + +```{r} +#| fig-width: 5 +#| fig-height: 3 +#| out-width: "80%" +test_prediction |> + ggplot(aes(.pred, .obs)) + +geom_point() + +geom_smooth(method = "lm", colour = "black") + +``` + + + +## 欢迎讨论！{.center} + + +`r rmdify::slideend(wechat = FALSE, type = "public", tel = FALSE, thislink = "https://drwater.rcees.ac.cn/course/public/RWEP/@PUB/SD/")` + diff --git a/SD/20240409_1_model/mpg-plot.png b/SD/20240409_1_model/mpg-plot.png new file mode 100644 index 0000000..de3439b Binary files /dev/null and b/SD/20240409_1_model/mpg-plot.png differ diff --git a/SD/20240409_2_大数据分析工具/_extensions b/SD/20240409_2_大数据分析工具/_extensions new file mode 120000 index 0000000..74119e3 --- /dev/null +++ b/SD/20240409_2_大数据分析工具/_extensions @@ -0,0 +1 @@ +../../_extensions \ No newline at end of file diff --git a/SD/20240402_2_正则表达式/index.qmd b/SD/20240409_2_大数据分析工具/index.qmd similarity index 94% rename from SD/20240402_2_正则表达式/index.qmd rename to SD/20240409_2_大数据分析工具/index.qmd index c9b8f8b..331c9e9 100644 --- a/SD/20240402_2_正则表达式/index.qmd +++ b/SD/20240409_2_大数据分析工具/index.qmd @@ -1,5 +1,5 @@ --- -title: "正则表达式" +title: "大数据分析工具" subtitle: 《区域水环境污染数据分析实践》
Data analysis practice of regional water environment pollution author: 苏命、王为东
中国科学院大学资源与环境学院
中国科学院生态环境研究中心 date: today @@ -144,6 +144,20 @@ babynames |> ![](../../image/data-science/transform.png) +## GNU/Linux服务器 + +- `ssh`, `scp` +- `bash` + - grep + - sed + - awk + - find + - xargs +- `Editor` + - `Virtual Studio Code` + - `Vim` + - `Emacs` + ## 欢迎讨论！{.center} diff --git a/_quarto.yml b/_quarto.yml index efd4286..c6b52a9 100644 --- a/_quarto.yml +++ b/_quarto.yml @@ -24,7 +24,7 @@ website: page-navigation: true page-footer: "Copyright 2024, [Ming Su](https://drwater.rcees.ac.cn)" navbar: - background: "grey" + background: "light" search: true right: - icon: house diff --git a/data/writexldemo.xlsx b/data/writexldemo.xlsx index 8906d98..d3402fe 100644 Binary files a/data/writexldemo.xlsx and b/data/writexldemo.xlsx differ