Advertisements

# StatQuest: Random Forests in R

library(ggplot2) library(cowplot) library(randomForest) ## NOTE: The data used in this demo comes from the UCI machine learning ## repository. ## http://archive.ics.uci.edu/ml/index.php ## Specifically, this is the heart disease data set. ## http://archive.ics.uci.edu/ml/datasets/Heart+Disease url <- "http://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/processed.cleveland.data" data <- read.csv(url, header=FALSE) ##################################### ## ## Reformat the data so that it is ## 1) Easy to use (add nice column names) ## 2) Interpreted correctly by randomForest.. ## ##################################### head(data) # you see data, but no column names colnames(data) <- c( "age", "sex",# 0 = female, 1 = male "cp", # chest pain # 1 = typical angina, # 2 = atypical angina, # 3 = non-anginal pain, # 4 = asymptomatic "trestbps", # resting blood pressure (in mm Hg) "chol", # serum cholestoral in mg/dl "fbs", # fasting blood sugar greater than 120 mg/dl, 1 = TRUE, 0 = FALSE "restecg", # resting electrocardiographic results # 1 = normal # 2 = having ST-T wave abnormality # 3 = showing probable or definite left ventricular hypertrophy "thalach", # maximum heart rate achieved "exang", # exercise induced angina, 1 = yes, 0 = no "oldpeak", # ST depression induced by exercise relative to rest "slope", # the slope of the peak exercise ST segment # 1 = upsloping # 2 = flat # 3 = downsloping "ca", # number of major vessels (0-3) colored by fluoroscopy "thal", # this is short of thalium heart scan # 3 = normal (no cold spots) # 6 = fixed defect (cold spots during rest and exercise) # 7 = reversible defect (when cold spots only appear during exercise) "hd" # (the predicted attribute) - diagnosis of heart disease # 0 if less than or equal to 50% diameter narrowing # 1 if greater than 50% diameter narrowing ) head(data) # now we have data and column names str(data) # this shows that we need to tell R which columns contain factors # it also shows us that there are some missing values. There are "?"s # in the dataset. ## First, replace "?"s with NAs. data[data == "?"] <- NA ## Now add factors for variables that are factors and clean up the factors ## that had missing data... data[data$sex == 0,]$sex <- "F" data[data$sex == 1,]$sex <- "M" data$sex <- as.factor(data$sex) data$cp <- as.factor(data$cp) data$fbs <- as.factor(data$fbs) data$restecg <- as.factor(data$restecg) data$exang <- as.factor(data$exang) data$slope <- as.factor(data$slope) data$ca <- as.integer(data$ca) # since this column had "?"s in it (which # we have since converted to NAs) R thinks that # the levels for the factor are strings, but # we know they are integers, so we'll first # convert the strings to integiers... data$ca <- as.factor(data$ca) # ...then convert the integers to factor levels data$thal <- as.integer(data$thal) # "thal" also had "?"s in it. data$thal <- as.factor(data$thal) ## This next line replaces 0 and 1 with "Healthy" and "Unhealthy" data$hd <- ifelse(test=data$hd == 0, yes="Healthy", no="Unhealthy") data$hd <- as.factor(data$hd) # Now convert to a factor str(data) ## this shows that the correct columns are factors and we've replaced ## "?"s with NAs because "?" no longer appears in the list of factors ## for "ca" and "thal" ##################################### ## ## Now we are ready to build a random forest. ## ##################################### set.seed(42) ## NOTE: For most machine learning methods, you need to divide the data ## manually into a "training" set and a "test" set. This allows you to train ## the method using the training data, and then test it on data it was not ## originally trained on. ## ## In contrast, Random Forests split the data into "training" and "test" sets ## for you. This is because Random Forests use bootstrapped ## data, and thus, not every sample is used to build every tree. The ## "training" dataset is the bootstrapped data and the "test" dataset is ## the remaining samples. The remaining samples are called ## the "Out-Of-Bag" (OOB) data. ## impute any missing values in the training set using proximities data.imputed <- rfImpute(hd ~ ., data = data, iter=6) ## NOTE: iter = the number of iterations to run. Breiman says 4 to 6 iterations ## is usually good enough. With this dataset, when we set iter=6, OOB-error ## bounces around between 17% and 18%. When we set iter=20, # set.seed(42) # data.imputed <- rfImpute(hd ~ ., data = data, iter=20) ## we get values a little better and a little worse, so doing more ## iterations doesn't improve the situation. ## ## NOTE: If you really want to micromanage how rfImpute(), ## you can change the number of trees it makes (the default is 300) and the ## number of variables that it will consider at each step. ## Now we are ready to build a random forest. ## NOTE: If the thing we're trying to predict (in this case it is ## whether or not someone has heart disease) is a continuous number ## (i.e. "weight" or "height"), then by default, randomForest() will set ## "mtry", the number of variables to consider at each step, ## to the total number of variables divided by 3 (rounded down), or to 1 ## (if the division results in a value less than 1). ## If the thing we're trying to predict is a "factor" (i.e. either "yes/no" ## or "ranked"), then randomForest() will set mtry to ## the square root of the number of variables (rounded down to the next ## integer value). ## In this example, "hd", the thing we are trying to predict, is a factor and ## there are 13 variables. So by default, randomForest() will set ## mtry = sqrt(13) = 3.6 rounded down = 3 ## Also, by default random forest generates 500 trees (NOTE: rfImpute() only ## generates 300 tress by default) model <- randomForest(hd ~ ., data=data.imputed, proximity=TRUE) ## RandomForest returns all kinds of things model # gives us an overview of the call, along with... # 1) The OOB error rate for the forest with ntree trees. # In this case ntree=500 by default # 2) The confusion matrix for the forest with ntree trees. # The confusion matrix is laid out like this: # # Healthy Unhealthy # -------------------------------------------------------------- # Healthy | Number of healthy people | Number of healthy people | # | correctly called "healthy" | incorectly called "unhealthy" | # | by the forest. | by the forest | # -------------------------------------------------------------- # Unhealthy| Number of unhealthy people | Number of unhealthy peole | # | incorrectly called | correctly called "unhealthy" | # | "healthy" by the forest | by the forest | # -------------------------------------------------------------- ## Now check to see if the random forest is actually big enough... ## Up to a point, the more trees in the forest, the better. You can tell when ## you've made enough when the OOB no longer improves. oob.error.data <- data.frame( Trees=rep(1:nrow(model$err.rate), times=3), Type=rep(c("OOB", "Healthy", "Unhealthy"), each=nrow(model$err.rate)), Error=c(model$err.rate[,"OOB"], model$err.rate[,"Healthy"], model$err.rate[,"Unhealthy"])) ggplot(data=oob.error.data, aes(x=Trees, y=Error)) + geom_line(aes(color=Type)) # ggsave("oob_error_rate_500_trees.pdf") ## Blue line = The error rate specifically for calling "Unheathly" patients that ## are OOB. ## ## Green line = The overall OOB error rate. ## ## Red line = The error rate specifically for calling "Healthy" patients ## that are OOB. ## NOTE: After building a random forest with 500 tress, the graph does not make ## it clear that the OOB-error has settled on a value or, if we added more ## trees, it would continue to decrease. ## So we do the whole thing again, but this time add more trees. model <- randomForest(hd ~ ., data=data.imputed, ntree=1000, proximity=TRUE) model oob.error.data <- data.frame( Trees=rep(1:nrow(model$err.rate), times=3), Type=rep(c("OOB", "Healthy", "Unhealthy"), each=nrow(model$err.rate)), Error=c(model$err.rate[,"OOB"], model$err.rate[,"Healthy"], model$err.rate[,"Unhealthy"])) ggplot(data=oob.error.data, aes(x=Trees, y=Error)) + geom_line(aes(color=Type)) # ggsave("oob_error_rate_1000_trees.pdf") ## After building a random forest with 1,000 trees, we get the same OOB-error ## 16.5% and we can see convergence in the graph. So we could have gotten ## away with only 500 trees, but we wouldn't have been sure that number ## was enough. ## If we want to compare this random forest to others with different values for ## mtry (to control how many variables are considered at each step)... oob.values <- vector(length=10) for(i in 1:10) { temp.model <- randomForest(hd ~ ., data=data.imputed, mtry=i, ntree=1000) oob.values[i] <- temp.model$err.rate[nrow(temp.model$err.rate),1] } oob.values ## [1] 0.1716172 0.1716172 0.1617162 0.1848185 0.1749175 0.1947195 0.1815182 ## [8] 0.2013201 0.1881188 0.1947195 ## The lowest value is when mtry=3, so the default setting was the best. ## Now let's create an MDS-plot to show how the samples are related to each ## other. ## ## Start by converting the proximity matrix into a distance matrix. distance.matrix <- dist(1-model$proximity) mds.stuff <- cmdscale(distance.matrix, eig=TRUE, x.ret=TRUE) ## calculate the percentage of variation that each MDS axis accounts for... mds.var.per <- round(mds.stuff$eig/sum(mds.stuff$eig)*100, 1) ## now make a fancy looking plot that shows the MDS axes and the variation: mds.values <- mds.stuff$points mds.data <- data.frame(Sample=rownames(mds.values), X=mds.values[,1], Y=mds.values[,2], Status=data.imputed$hd) ggplot(data=mds.data, aes(x=X, y=Y, label=Sample)) + geom_text(aes(color=Status)) + theme_bw() + xlab(paste("MDS1 - ", mds.var.per[1], "%", sep="")) + ylab(paste("MDS2 - ", mds.var.per[2], "%", sep="")) + ggtitle("MDS plot using (1 - Random Forest Proximities)")

# StatQuest: PCA in Python

## NOTE: This is Python 3 code. import pandas as pd import numpy as np import random as rd from sklearn.decomposition import PCA from sklearn import preprocessing import matplotlib.pyplot as plt # NOTE: This was tested with matplotlib v. 2.1.0 ######################### # # Data Generation Code # ######################### ## In this example, the data is in a data frame called data. ## Columns are individual samples (i.e. cells) ## Rows are measurements taken for all the samples (i.e. genes) ## Just for the sake of the example, we'll use made up data... genes = ['gene' + str(i) for i in range(1,101)] wt = ['wt' + str(i) for i in range(1,6)] ko = ['ko' + str(i) for i in range(1,6)] data = pd.DataFrame(columns=[*wt, *ko], index=genes) for gene in data.index: data.loc[gene,'wt1':'wt5'] = np.random.poisson(lam=rd.randrange(10,1000), size=5) data.loc[gene,'ko1':'ko5'] = np.random.poisson(lam=rd.randrange(10,1000), size=5) print(data.head()) print(data.shape) ######################### # # Perform PCA on the data # ######################### # First center and scale the data scaled_data = preprocessing.scale(data.T) pca = PCA() # create a PCA object pca.fit(scaled_data) # do the math pca_data = pca.transform(scaled_data) # get PCA coordinates for scaled_data ######################### # # Draw a scree plot and a PCA plot # ######################### #The following code constructs the Scree plot per_var = np.round(pca.explained_variance_ratio_* 100, decimals=1) labels = ['PC' + str(x) for x in range(1, len(per_var)+1)] plt.bar(x=range(1,len(per_var)+1), height=per_var, tick_label=labels) plt.ylabel('Percentage of Explained Variance') plt.xlabel('Principal Component') plt.title('Scree Plot') plt.show() #the following code makes a fancy looking plot using PC1 and PC2 pca_df = pd.DataFrame(pca_data, index=[*wt, *ko], columns=labels) plt.scatter(pca_df.PC1, pca_df.PC2) plt.title('My PCA Graph') plt.xlabel('PC1 - {0}%'.format(per_var[0])) plt.ylabel('PC2 - {0}%'.format(per_var[1])) for sample in pca_df.index: plt.annotate(sample, (pca_df.PC1.loc[sample], pca_df.PC2.loc[sample])) plt.show() ######################### # # Determine which genes had the biggest influence on PC1 # ######################### ## get the name of the top 10 measurements (genes) that contribute ## most to pc1. ## first, get the loading scores loading_scores = pd.Series(pca.components_[0], index=genes) ## now sort the loading scores based on their magnitude sorted_loading_scores = loading_scores.abs().sort_values(ascending=False) # get the names of the top 10 genes top_10_genes = sorted_loading_scores[0:10].index.values ## print the gene names and their scores (and +/- sign) print(loading_scores[top_10_genes])<span id="mce_SELREST_start" style="overflow:hidden;line-height:0;"></span>