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   Random Forest is a powerful ensemble learning method that can be applied to various prediction tasks, in particular classification and regression. The method uses an ensemble of decision trees as a basis and therefore has all advantages of decision trees, such as high accuracy, easy usage, and no necessity of scaling data. Moreover, it also has a very important additional benefit, namely perseverance to overfitting (unlike simple decision trees) as the trees are combined. In this tutorial, we will try to predict the value of diamonds from the Diamonds dataset (part of ggplot2) applying a Random Forest Regressor in R. We further visualize and analyze the obtained predictive model and look into the tuning of hyperparameters and the importance of available features. Loading and preparing data # Import the dataset diamond <-diamonds head(diamond) ## # A tibble: 6 x 10 ## carat cut color clarity depth table price x y z ## <dbl> <ord> <ord> <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl> ## 1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98 2.43 ## 2 0.21 Premium E SI1 59.8 61 326 3.89 3.84 2.31 ## 3 0.23 Good E VS1 56.9 65 327 4.05 4.07 2.31 ## 4 0.290 Premium I VS2 62.4 58 334 4.2 4.23 2.63 ## 5 0.31 Good J SI2 63.3 58 335 4.34 4.35 2.75 ## 6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96 2.48 The dataset contains information on 54,000 diamonds. It contains the price as well as 9 other attributes. Some features are in the text format, and we need to encode them to the numerical format. Let’s also drop the unnamed index column. # Convert the variables to numerical diamond$cut <- as.integer(diamond$cut) diamond$color <-as.integer(diamond$color) diamond$clarity <- as.integer(diamond$clarity) head(diamond) ## # A tibble: 6 x 10 ## carat cut color clarity depth table price x y z ## <dbl> <int> <int> <int> <dbl> <dbl> <int> <dbl> <dbl> <dbl> ## 1 0.23 5 2 2 61.5 55 326 3.95 3.98 2.43 ## 2 0.21 4 2 3 59.8 61 326 3.89 3.84 2.31 ## 3 0.23 2 2 5 56.9 65 327 4.05 4.07 2.31 ## 4 0.290 4 6 4 62.4 58 334 4.2 4.23 2.63 ## 5 0.31 2 7 2 63.3 58 335 4.34 4.35 2.75 ## 6 0.24 3 7 6 62.8 57 336 3.94 3.96 2.48 As we already mentioned, one of the benefits of the Random Forest algorithm is that it doesn’t require data scaling. So, to use this algorithm, we only need to define features and the target that we are trying to predict. We could potentially create numerous features by combining the available attributes. For simplicity, we will not do that now. If you are trying to build the most accurate model, feature creation is definitely a key part and substantial time should be invested in creating features (e.g. through interaction). # Create features and target X <- diamond %>% select(carat, depth, table, x, y, z, clarity, cut, color) y <- diamond$price Training the model and making predictions At this point, we have to split our data into training and test sets. As a training set, we will take 75% of all rows and use 25% as test data. # Split data into training and test sets index <- createDataPartition(y, p=0.75, list=FALSE) X_train <- X[ index, ] X_test <- X[-index, ] y_train <- y[index] y_test<-y[-index] # Train the model regr <- randomForest(x = X_train, y = y_train , maxnodes = 10, ntree = 10) Now, we have a pre-trained model and can predict values for the test data. We then compare the predicted value with the actual values in the test data and analyze the accuracy of the model. To make this comparison more illustrative, we will show it both in the forms of table and plot the price and the carat value # Make prediction predictions <- predict(regr, X_test) result <- X_test result['price'] <- y_test result['prediction']<- predictions head(result) ## # A tibble: 6 x 11 ## carat depth table x y z clarity cut color price prediction ## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <int> <int> <int> <dbl> ## 1 0.24 62.8 57 3.94 3.96 2.48 6 3 7 336 881. ## 2 0.23 59.4 61 4 4.05 2.39 5 3 5 338 863. ## 3 0.2 60.2 62 3.79 3.75 2.27 2 4 2 345 863. ## 4 0.32 60.9 58 4.38 4.42 2.68 1 4 2 345 863. ## 5 0.3 62 54 4.31 4.34 2.68 2 5 6 348 762. ## 6 0.3 62.7 59 4.21 4.27 2.66 3 3 7 351 863. # Import library for visualization library(ggplot2) # Build scatterplot ggplot( ) + geom_point( aes(x = X_test$carat, y = y_test, color = 'red', alpha = 0.5) ) + geom_point( aes(x = X_test$carat , y = predictions, color = 'blue', alpha = 0.5)) + labs(x = "Carat", y = "Price", color = "", alpha = 'Transperency') + scale_color_manual(labels = c( "Predicted", "Real"), values = c("blue", "red")) The figure displays that predicted prices (blue scatters) coincide well with the real ones (red scatters), especially in the region of small carat values. But to estimate our model more precisely, we will look at Mean absolute error (MAE), Mean squared error (MSE), and R-squared scores. # Import library for Metrics library(Metrics) ## ## Attaching package: 'Metrics' ## The following objects are masked from 'package:caret': ## ## precision, recall print(paste0('MAE: ' , mae(y_test,predictions) )) ## [1] "MAE: 742.401258870433" print(paste0('MSE: ' ,caret::postResample(predictions , y_test)['RMSE']^2 )) ## [1] "MSE: 1717272.6547428" print(paste0('R2: ' ,caret::postResample(predictions , y_test)['Rsquared'] )) ## [1] "R2: 0.894548902990278" We obtain high error values (MAE and MSE). To improve the predictive power of the model, we should tune the hyperparameters of the algorithm. We can do this manually, but it will take a lot of time. In order to tune the parameters ntrees (number of trees in the forest) and maxnodes (maximum number of terminal nodes trees in the forest can have), we will need to build a custom Random Forest model to obtain the best set of parameters for our model and compare the output for various combinations of the parameters. Tuning the parameters # If training the model takes too long try setting up lower value of N N=500 #length(X_train) X_train_ = X_train[1:N , ] y_train_ = y_train[1:N] seed <-7 metric<-'RMSE' customRF <- list(type = "Regression", library = "randomForest", loop = NULL) customRF$parameters <- data.frame(parameter = c("maxnodes", "ntree"), class = rep("numeric", 2), label = c("maxnodes", "ntree")) customRF$grid <- function(x, y, len = NULL, search = "grid") {} customRF$fit <- function(x, y, wts, param, lev, last, weights, classProbs, ...) { randomForest(x, y, maxnodes = param$maxnodes, ntree=param$ntree, ...) } customRF$predict <- function(modelFit, newdata, preProc = NULL, submodels = NULL) predict(modelFit, newdata) customRF$prob <- function(modelFit, newdata, preProc = NULL, submodels = NULL) predict(modelFit, newdata, type = "prob") customRF$sort <- function(x) x[order(x[,1]),] customRF$levels <- function(x) x$classes # Set grid search parameters control <- trainControl(method="repeatedcv", number=10, repeats=3, search='grid') # Outline the grid of parameters tunegrid <- expand.grid(.maxnodes=c(70,80,90,100), .ntree=c(900, 1000, 1100)) set.seed(seed) # Train the model rf_gridsearch <- train(x=X_train_, y=y_train_, method=customRF, metric=metric, tuneGrid=tunegrid, trControl=control) Let’s visualize the impact of tuned parameters on RMSE. The plot shows how the model’s performance develops with different variations of the parameters. For values maxnodes: 80 and ntree: 900, the model seems to perform best. We would now use these parameters in the final model. plot(rf_gridsearch) Best parameters: rf_gridsearch$bestTune ## maxnodes ntree ## 5 80 1000 Defining and visualizing variables importance For this algorithm, we used all available diamond features, but some of them contain more predictive power than others. Let’s build the plot with features list on the y axis. On the X axis we’ll have incremental decrease in node impurities from splitting on the variable, averaged over all trees, it is measured by the residual sum of squares and therefore gives us a rough idea about the predictive power of the feature. Generally, it is important to keep in mind, that random forest does not allow for any causal interpretation. varImpPlot(rf_gridsearch$finalModel, main ='Feature importance') From the figure above you can see that the size of diamond (x,y,z refer to length, width, depth) and the weight (carat) contain the major part of the predictive power.
For the past few years, tasks involving text and speech processing have become really hot-trendy. Among the various researches belonging to the fields of Natural Language Processing and Machine Learning, sentiment analysis ranks really high. Sentiment analysis allows identifying and getting subjective information from the source data using data analysis and visualization, ML models for classification, text mining and analysis. This helps to understand social opinions on the subject, so sentiment analysis is widely used in business and politics and usually conducted in social networks. Social networks as the main resource for sentiment analysis Nowadays, social nets and forums are the main stage for people sharing opinions. That is why they are so interesting for researches to figure out the attitude to one or another object. Sentiment analysis allows challenging the problem of analyzing textual data created by users on social nets, microblogging platforms and forums, as well as business platforms regarding the opinions the users have about some product, service, person, idea, and so on. In the most common approach, text can be classified into two classes (binary sentiment classification): positive and negative, but sentiment analysis can have way more classes involving multi-class problem. Sentiment analysis allows processing hundreds and thousands of texts in a short period of time. This is another reason for its popularity - while people need many hours to do the same work, sentiment analysis is able to finish it in a few seconds.  Common approaches for classifying sentiments Sentiment analysis of the text data can be done via three commonly used methods: machine learning, using dictionaries, and hybrid. Learning-based approach Machine learning approach is one of the most popular nowadays. Using ML techniques and various methods, users can build a classifier that can identify different sentiment expressions in the text. Dictionary-based approach The main concept of this approach is using a bag of words with polarity scores, that can help to establish whether the word has a positive, negative, or neutral connotation. Such an approach doesn't require any training set to be used allowing to classify even a small amount of data. However, there are a lot of words and expressions that are still not included in sentiment dictionaries. Hybrid approach As is evident from the title, this approach combines machine learning and lexicon-based techniques. Despite the fact that it's not widely used, the hybrid approach shows more promising and valuable results than the two approaches used separately. In this article, we will implement a dictionary-based approach, so let's deep into its basis. Dictionary (or Lexicon)-based sentiment analysis uses special dictionaries, lexicons, and methods, a lot number of which is available for calculating sentiment in text. The main are: afinn bing nrc All three are the sentiment dictionaries which help to evaluate the valence of the textual data by searching for words that describe emotion or opinion.  Things needed to be done before sentiment analysis Before starting building sentiment analyzer, a few steps must be taken. First of all, we need to state the problem we are going to explore, understand its objective. Since we will use data from Donald Trump twitter, let’s claim our objective as an attempt to analyze which connotation his last tweets have. As the problem is outlined, we need to prepare our data for examining. Data preprocessing is basically an initial step in text and sentiment classification. Depending on the input data, various amount of techniques can be applied in order to make data more comprehensible and improve the effectiveness of the analysis. The most common steps in data processing are: removing numbers removing stopwords removing punctuation and so on. Building sentiment classifier The first step in building our classifier is installing the needed packages. We will need the following packages that can be installed via command install.packages("name of the package") directly from the development environment: twitteR dplyr splitstackshape purrr As soon as all the packages are installed, let's initialize them. In [6]: library(twitteR) library(dplyr) library(splitstackshape) library(tidytext) library(purrr) We're going to get tweets from Donald Trump's account directly from Twitter, and that is why we need to provide Twitter API credentials. In [26]: api_key <- "----" api_secret <- "----" access_token <- "----" access_token_secret <- "----" In [27]: setup_twitter_oauth(api_key,api_secret,access_token,access_token_secret) [1] "Using direct authentication" And now it's time to get tweets from Donald Trump's account and convert the data into dataframe. In [114]: TrumpTweets <- userTimeline("realDonaldTrump", n = 3200) In [115]: TrumpTweets <- tbl_df(map_df(TrumpTweets, Here how our initial dataframe looks: In [116]: head(TrumpTweets)   text favorited favoriteCount replyToSN created truncated replyToSID id replyToUID statusSource screenName retweetCount isRetweet retweeted longitude latitude It was my great honor to host Canadian Prime Minister @JustinTrudeau at the @WhiteHouse today!🇺🇸🇨🇦 FALSE 17424 NA 2019-06-20 17:49:52 FALSE NA 1141765119929700353 NA <a href="" rel="nofollow">Twitter for iPhone</a> realDonaldTrump 3540 FALSE FALSE NA NA Iran made a very big mistake! FALSE 127069 NA 2019-06-20 14:15:04 FALSE NA 1141711064305983488 NA <a href="" rel="nofollow">Twitter for iPhone</a> realDonaldTrump 38351 FALSE FALSE NA NA “The President has a really good story to tell. We have unemployment lower than we’ve seen in decades. We have peop… FALSE 36218 NA 2019-06-20 14:14:13 TRUE NA 1141710851617034240 NA <a href="" rel="nofollow">Twitter for iPhone</a> realDonaldTrump 8753 FALSE FALSE NA NA S&amp;P opens at Record High! FALSE 43995 NA 2019-06-20 13:58:53 FALSE NA 1141706991464849408 NA <a href="" rel="nofollow">Twitter for iPhone</a> realDonaldTrump 9037 FALSE FALSE NA NA Since Election Day 2016, Stocks up almost 50%, Stocks gained 9.2 Trillion Dollars in value, and more than 5,000,000… FALSE 62468 NA 2019-06-20 00:12:31 TRUE NA 1141499029727121408 NA <a href="" rel="nofollow">Twitter for iPhone</a> realDonaldTrump 16296 FALSE FALSE NA NA Congratulations to President Lopez Obrador — Mexico voted to ratify the USMCA today by a huge margin. Time for Congress to do the same here! FALSE 85219 NA 2019-06-19 23:01:59 FALSE NA 1141481280653209600 NA <a href="" rel="nofollow">Twitter for iPhone</a> realDonaldTrump 20039 FALSE FALSE NA NA To prepare our data for classification, let's get rid of links and format dataframe in a way when only one word is in line. In [117]: TrumpTweets <- TrumpTweets[-(grep('', TrumpTweets$'text')),] In [118]: TrumpTweets$'tweet' <- 'tweet' TrumpTweets <- TrumpTweets[ , c('text', 'tweet')] TrumpTweets <- unnest_tokens(TrumpTweets, words, text) And this is how our dataframe looks now: In [119]: tail(TrumpTweets) tweet words tweet most tweet of tweet their tweet people tweet from tweet venezuela In [120]: head(TrumpTweets) tweet words tweet iran tweet made tweet a tweet very tweet big tweet mistake It's obvious that dataframe also contains various words without useful content. So it's a good idea to get rid of them. In [121]: TrumpTweets <- anti_join(TrumpTweets, stop_words, by = c('words' = 'word')) And here's the result: In [122]: tail(TrumpTweets) tweet words tweet harassment tweet russia tweet informed tweet removed tweet people tweet venezuela In [123]: head(TrumpTweets) tweet words tweet iran tweet mistake tweet amp tweet record tweet congratulations tweet president Much better, isn't it? Let's see how many times each word appears in Donald Trump's tweets. In [124]: word_count <- dplyr::count(TrumpTweets, words, sort = TRUE) In [125]: head(word_count) words n day 2 democrats 2 enjoy 2 florida 2 iran 2 live 2 Now it's time to create some dataframe with sentiments that will be used for tweets classification. We will use bing dictionary although you can easily use any other source. In [126]: sentiments <-get_sentiments("bing") sentiments <- dplyr::select(sentiments, word, sentiment) In [127]: TrumpTweets_sentiments <- merge(word_count, sentiments, by.x = c('words'), by.y = c('word')) Above we did a simple classification of Trump's tweets words using our sentiment bag of words. And this is how the result looks: In [128]: TrumpTweets_sentiments words n sentiment beautiful 1 positive burning 1 negative congratulations 1 positive defy 1 negative enjoy 2 positive harassment 1 negative hell 1 negative limits 1 negative mistake 1 negative scandal 1 negative strong 1 positive trump 1 positive Let's look at the number of occurrences per sentiment in tweets. In [131]: sentiments_count <- dplyr::count(TrumpTweets_sentiments, sentiment, sort = TRUE) In [132]: sentiments_count sentiment n negative 7 positive 5 We also may want to know the total count and percentage of all the sentiments. In [133]: sentiments_sum <- sum(sentiments_count$'n') In [134]: sentiments_count$'percentage' <- sentiments_count$'n' / sentiments_sum Let's now create an ordered dataframe for plotting counts of sentiments. In [135]: sentiment_count <- rbind(sentiments_count) In [136]: sentiment_count <- sentiment_count[order(sentiment_count$sentiment), ] In [137]: sentiment_count sentiment n percentage negative 7 0.5833333 positive 5 0.4166667 And now it's time for the visualization. We will plot the results of our classifier. In [144]: sentiment_count$'colour' <- as.integer(4) In [145]: barplot(sentiment_count$'n', names.arg = sentiment_count$'sentiment', col = sentiment_count$'colour', cex.names = .5) In [146]: barplot(sentiment_count$'percentage', names.arg = sentiment_count$'sentiment', col = sentiment_count$'colour', cex.names = .5)     Conclusion Sentiment analysis is a great way to explore emotions and opinions among the people. Today we explored the most common and easy way for sentiment analysis that is still great in its simplicity and gives quite an informative result. However, it should be noted that different sentiment analysis methods and lexicons work better depending on the problem and text corpuses. The result of the dictionary-based approach also depends much on the matching between the used dictionary and the textual data needed to be classified. But still, user can create own dictionary that can be a good solution. Despite this, dictionary-based methods usually show much better results than more compound techniques.
Introduction Nowadays PostgreSQL is probably one of the most powerful relational databases among the open-source solutions. Its functional capacities are no worse than Oracle’s and definitely way ahead of the MySQL. So if you are working on apps using Python, someday you will face the need of working with databases. Luckily, Python has quite a wide amount of packages that provide an easy way of connecting and using databases. In this article, we will go through the most common packages for dealing with PostgreSQL and show the way of using one of them. Python modules for working with PostgreSQL The way you access the database via Python environment usually depends on personal choices and development specificities. One of the most common and easiest ways is to establish connections using special Python drivers among which the most popular are psycopg2, PyGreSQL, py-postgresql, and pg8000. psycopg2 psycopg2 is probably one of the most popular packages for interaction with PostgreSQL from the Python environment. Written on C programming language with libpq wrapper, it provides a wide range of operations for database manipulations. psycopg2 provides both client-side and server-side cursors as well as asynchronous communication and notifications and a “COPY TO/ FROM” support. It should be noted that the package provides most of Python data types support with their adaptation to match PostgreSQL data types. Among the key features of this package are the support of multiple connections and connection objects, various methods of transaction and its management, pool of connections, auto filtering and async queries and cursors objects. Plus, columns from the database are returned via Python dictionary with their names. Among the possible weaknesses of the package, we can note a lack of documentation and mostly outdated code examples. PyGreSQL PyGreSQL is the first PostgreSQL adapter and one of the open-source Python modules for working with PostgreSQL databases that is actively developing. This package embeds the PostgreSQL query library to provide easy use of all of the PostgreSQL database manipulation features from Python environment. Despite the fact that this module provides great opportunities for working with databases, some users found its problems with working with cursors objects. py-postgresql py-postgresql is a Python3 module that provides wide abilities to interact with PostgreSQL including a high-level driver to make working with databases deeper. Among the main disadvantages of this package are the fact that it works only on the Python3 environment and doesn’t have direct support for high-level async interfaces. pg8000 The pg8000 is a pure Python module for dealing with PostgreSQL interactions that has wide documentation and is actively developing. It should be noted that this module complies with Python Database API 2.0 that basically provides the user a broader reach of DB connectivity from Python environment. Being pure Python, the module allows users using it in AWS Lambda functions without any extra work. For now, this module is actively reviewing and developing and therefore some bugs can be faced while working with it. Despite the fact that many more drivers for PostgreSQL interacting are available, some of them are outdated or have limits in usage. Still, the listed above provide users great opportunities for various databases manipulations. Working with queries We covered some of the most common packages for connecting PostgreSQL to the Python app and now it’s time to see it in use. We chose for working the psycopg2 module and continue reviewing connecting and working with databases with its operations as an example. It is assumed that PostgreSQL is already installed and run on your machine. 1. Installing package. Run conda install psycopg2 if you are using anaconda or pip install psycopg2 via pip package manager. 2. Once the package was downloaded, let’s check the accessibility to the database.  To check the connection, we will write a simple script. Be sure, that you provided own specific database name, username, and password. Save the script and run it with Python command. As a result, we will get an empty array ([]) symbolizing that connection works properly. Now it’s time to explore some basic operations with databases. First, let’s add to our database some tables we will work with. Usually data stores in tables in .csv format so why not start exploring PostgreSQL interactions from adding some data to database from .csv table? For the next examples, we will use parts of the datasets that can be found on Kaggle:  Chicago census data; Chicago public schools. First, we establish connection and create tables with the corresponding table names. connect_str = "dbname=yourdbname user=yourname host='localhost' " + "password='yourpwd'" conn = psycopg2.connect(connect_str) cursor = conn.cursor() cursor.execute(""" CREATE TABLE census(id integer PRIMARY KEY,  COMMUNITY_AREA_NUMBER float,  COMMUNITY_AREA_NAME text,  PERCENT_OF_HOUSING_CROWDED float,  PERCENT_HOUSEHOLDS_BELOW_POVERTY float,  PERCENT_AGED_16+_UNEMPLOYED float,  PERCENT_AGED_25+_WITHOUT_HIGH_SCHOOL_DIPLOMA float,  PERCENT_AGED_UNDER_18_OR_OVER_64 float,  PER_CAPITA_INCOME integer,  HARDSHIP_INDEX float) """) In such a way another table will be created. After this, we can copy data from corresponding .csv files to our tables. with open('CENSUS.csv', 'r') as f: next(f) cursor.copy_from(f, census, sep=',') conn.commit() The commit() method is needed to make changes to our database persistent. It should be noted that while copying data from .csv tables, we do not need a header, so we skip it. The same operation will be done to another table. And now we can write some simple queries. Let’s find out which Community Areas in CENSUS table start with letter ‘B’. sql = ‘SELECT community_area_name FROM census WHERE community_area_name LIKE %s’ community_area_name = ‘B%’ cursor.execute(“sql, (community_area_name)”) cursor.fetchall() Let’s list the top 5 Community Areas by average College Enrollment. sql =  ‘SELECT community_area_name, AVG(college_enrollment) AS avg_enrollment FROM schools GROUP BY community_area_name limit 5’ cursor.execute(“sql”) cursor.fetchall() Now, let’s find the Per Capita Income of the Community Area which has a school Safety Score of 1. sql = SELECT community_area_number, community_area_name, per_capita_income FROM census WHERE community_area_number IN ( select community_area_number FROM schools WHERE safety_score = '1' )’ cursor.execute(“sql”) As you have already noticed, we used fetchall() method. The package has some quite useful - among many other useful methods - options to retrieve a single row from a table (fetchone()) or all the rows according to query (fetchall()). As soon as all the manipulations with the database are finished, the connection must be closed. It can be done with two methods: cursor.close() and conn.close(). Conclusion Establishing connection and interaction with PostgreSQL from the Python environment with the help of various drivers becomes easy and quick. Every module has its own pros and cons that basically allows each user to choose the suitable one. The psycopg2 module used for the example provides an easy and clear way to establish a connection and set queries giving opportunities for using flexible queries.
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