Regression

cpp/linear_regression/avocado_price_prediction/Makefile

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+# This is a simple Makefile used to build the example source code.
+# This example might requires some modifications in order to work correctly on
+# your system.
+# If you're not using the Armadillo wrapper, replace `armadillo` with linker commands
+# for the BLAS and LAPACK libraries that you are using.
+
+TARGET := avocado_price_prediction
+SRC := avocado_price_prediction.cpp
+LIBS_NAME := armadillo
+
+CXX := g++
+CXXFLAGS += -std=c++17 -Wall -Wextra -O3 -DNDEBUG -fopenmp
+# Use these CXXFLAGS instead if you want to compile with debugging symbols and
+# without optimizations.
+# CXXFLAGS += -std=c++17 -Wall -Wextra -g -O0
+
+LDFLAGS  += -fopenmp
+# Add header directories for any includes that aren't on the
+# default compiler search path.
+INCLFLAGS := -I .
+# If you have mlpack or ensmallen installed somewhere nonstandard, uncomment and
+# update the lines below.
+# Uncomment the following if you are using the Scatter function for plotting
+# INCLFLAGS += -I/usr/include/python3.11
+# INCLFLAGS += -I/path/to/ensmallen/include/
+CXXFLAGS += $(INCLFLAGS)
+
+OBJS := $(SRC:.cpp=.o)
+LIBS := $(addprefix -l,$(LIBS_NAME))
+CLEAN_LIST := $(TARGET) $(OBJS)
+
+# default rule
+default: all
+
+$(TARGET): $(OBJS)
+	$(CXX) $(OBJS) -o $(TARGET) $(LDFLAGS) $(LIBS)
+
+.PHONY: all
+all: $(TARGET)
+
+.PHONY: clean
+clean:
+	@echo CLEAN $(CLEAN_LIST)
+	@rm -f $(CLEAN_LIST)

cpp/linear_regression/avocado_price_prediction/avocado_price_prediction.cpp

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+/**
+* Predicting Avocado's Average Price using Linear Regression
+* Our target is to predict the future price of avocados depending on various
+* features (Type, Region, Total Bags, ...).
+*
+* Dataset
+* 
+* Avocado Prices dataset has the following features:
+* PLU - Product Lookup Code in Hass avocado board.
+* Date - The date of the observation.
+* AveragePrice - Observed average price of single avocado.
+* Total Volume - Total number of avocado's sold.
+* 4046 - Total number of avocado's with PLU 4046 sold.
+* 4225 - Total number of avocado's with PLU 4225 sold.
+* 4770 - Total number of avocado's with PLU 4770 sold.
+* Total Bags = Small Bags + Large Bags + XLarge Bags.
+* Type - Conventional or organic.
+* Year - Year of observation.
+* Region - City or region of observation.
+*
+* Approach
+* 
+* In this example, first we will do EDA on the dataset to find correlation
+* between various features.
+* Then we'll be using onehot encoding to encode categorical features.
+* Finally we will use LinearRegression API from mlpack to learn the correlation
+* between various features and the target i.e AveragePrice.
+* After training the model, we will use it to do some predictions, followed by
+* various evaluation metrics to quantify how well our model behaves.
+*/
+#include <mlpack.hpp>
+using namespace mlpack;
+using namespace mlpack::data;
+
+//Drop the dataset header using sed, sed is a Unix utility that parses and transforms text."
+//!mkdir -p data && cat avocado.csv | sed 1d > avocado_trim.csv"
+//"Drop columns 1 and 2 (\"Unamed: 0\", \"Date\") as these are not required and their presence cause issues while loading the data."
+//!rm avocado_trim.csv"
+//"!mv avocado_trim2.csv avocado_trim.csv"
+
+int main()
+{
+ //!wget -q https://datasets.mlpack.org/avocado.csv.gz"
+ // Features 9 (Avocado type) and 11 (region of observation) are strings
+ // (categorical), but armadillo matrices can contain only numeric information;
+ // so, we have to explicitly define them as categorical in `datasetInfo`
+ // this allows mlpack to map numeric values to each of those values,
+ // which can later be unmapped to strings.
+ // Load the dataset into armadillo matrix.
+
+ arma::mat matrix;
+ data::DatasetInfo info;
+ info.Type(9) = data::Datatype::categorical;
+ info.Type(11) = data::Datatype::categorical;
+ data::Load("../../../data/avocado.csv", matrix, info);
+ // Printing header for dataset.
+ std::cout << std::setw(10) << "AveragePrice" << std::setw(14) 
+     << "Total Volume" << std::setw(9) << "4046" << std::setw(13) 
+     << "4225" << std::setw(13) << "4770" << std::setw(17) << "Total Bags" 
+     << std::setw(13) << "Small Bags" << std::setw(13) << "Large Bags" 
+     << std::setw(17) << "XLarge Bags" << std::setw(10) << "Type" 
+     << std::setw(10) << "Year" << std::setw(15) << "Region" <<  std::endl;
+ std::cout << matrix.submat(0, 0, matrix.n_rows-1, 5).t() << std::endl;
+ // Exploratory Data Analysis
+ arma::mat output;
+ data::OneHotEncoding(matrix, output, info);
+ arma::Row<double> targets = arma::conv_to<arma::Row<double>>::from(output.row(0));
+ // Labels are dropped from the originally loaded data to be used as features.
+ output.shed_row(0);
+ // Train Test Split,
+ // The dataset has to be split into a training set and a test set. Here the
+ // dataset has 18249 observations and the `testRatio` is set to 20% of the
+ // total observations. This indicates the test set should have
+ // 20% * 18249 = 3649 observations and training test should have
+ // 14600 observations respectively.
+ arma::mat Xtrain;
+ arma::mat Xtest;
+ arma::Row<double> Ytrain;
+ arma::Row<double> Ytest;
+ data::Split(output, targets, Xtrain, Xtest, Ytrain, Ytest, 0.2);
+ // Convert armadillo Rows into rowvec. (Required by mlpacks' LinearRegression API in this format).
+ arma::rowvec yTrain = arma::conv_to<arma::rowvec>::from(Ytrain);
+ arma::rowvec yTest = arma::conv_to<arma::rowvec>::from(Ytest);
+
+ /* Training the linear model.
+  * Regression analysis is the most widely used method of prediction.
+  * Linear regression is used when the dataset has a linear correlation
+  * and as the name suggests, multiple linear regression has one independent
+  * variable (predictor) and one or more dependent variable(response).
+  * The simple linear regression equation is represented as
+  * y = $a + b_{1}x_{1} + b_{2}x_{2} + b_{3}x_{3} + ... + b_{n}x_{n}$
+  * where $x_{i}$ is the ith explanatory variable, y is the dependent
+  * variable, $b_{i}$ is ith coefficient and a is the intercept.
+  * To perform linear regression we'll be using the `LinearRegression` class from mlpack.
+  * Create and train Linear Regression model.
+ */
+ LinearRegression lr(Xtrain, yTrain, 0.5);
+ arma::rowvec yPreds;
+ lr.Predict(Xtest, yPreds);
+ // Save the yTest and yPreds into csv for generating plots.
+ arma::mat preds;
+ preds.insert_rows(0, yTest);
+ preds.insert_rows(1, yPreds);
+ arma::mat histpreds = yTest - yPreds;
+ mlpack::data::Save("./data/predictions.csv", preds);
+ mlpack::data::Save("./data/predsDiff.csv", yPreds);
+ /*
+  * Model Evaluation,
+  * Test data is visualized with `yTest` and `yPreds`, the blue points
+  * indicates the data points and the blue line indicates the regression
+  * line or best fit line.
+  * Evaluation Metrics for Regression model,
+  * In the previous cell we have visualized our model performance by plotting
+  * the best fit line. Now we will use various evaluation metrics to understand
+  * how well our model has performed.
+  * Mean Absolute Error (MAE) is the sum of absolute differences between actual
+  * and predicted values, without considering the direction.
+  * MAE = \\frac{\\sum_{i=1}^n\\lvert y_{i} - \\hat{y_{i}}\\rvert} {n}
+  * Mean Squared Error (MSE) is calculated as the mean or average of the
+  * squared differences between predicted and expected target values in
+  * a dataset, a lower value is better
+  * MSE = \\frac {1}{n} \\sum_{i=1}^n (y_{i} - \\hat{y_{i}})^2,
+  * Root Mean Squared Error (RMSE), Square root of MSE yields root mean square
+  * error (RMSE) it indicates the spread of the residual errors. It is always
+  * positive, and a lower value indicates better performance.
+  * RMSE = \\sqrt{\\frac {1}{n} \\sum_{i=1}^n (y_{i} - \\hat{y_{i}})^2}
+ */
+ // Model evaluation metrics.
+ // From the above metrics, we can notice that our model MAE is ~0.2,
+ // which is relatively small compared to our average price of $1.405,
+ // from this and the above plot we can conclude our model is a reasonably
+ // good fit.
+
+ std::cout << "Mean Absolute Error: "
+           << arma::mean(arma::abs(yPreds - yTest)) << std::endl;
+ std::cout << "Mean Squared Error: "
+           << arma::mean(arma::pow(yPreds - yTest,2)) << std::endl;
+ std::cout << "Root Mean Squared Error: "
+           << sqrt(arma::mean(arma::pow(yPreds - yTest,2))) << std::endl;
+} 

cpp/linear_regression/california_housing_price_prediction/Makefile

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+# This is a simple Makefile used to build the example source code.
+# This example might requires some modifications in order to work correctly on
+# your system.
+# If you're not using the Armadillo wrapper, replace `armadillo` with linker commands
+# for the BLAS and LAPACK libraries that you are using.
+
+TARGET := california-house-price-prediction 
+SRC := california_house_price_prediction.cpp
+LIBS_NAME := armadillo
+
+CXX := g++
+CXXFLAGS += -std=c++17 -Wall -Wextra -O3 -DNDEBUG -fopenmp
+# Use these CXXFLAGS instead if you want to compile with debugging symbols and
+# without optimizations.
+# CXXFLAGS += -std=c++17 -Wall -Wextra -g -O0
+
+LDFLAGS  += -fopenmp
+# Add header directories for any includes that aren't on the
+# default compiler search path.
+INCLFLAGS := -I .
+# If you have mlpack or ensmallen installed somewhere nonstandard, uncomment and
+# update the lines below.
+# Uncomment the following if you are using the Scatter function for plotting
+# INCLFLAGS += -I/usr/include/python3.11
+# INCLFLAGS += -I/path/to/ensmallen/include/
+CXXFLAGS += $(INCLFLAGS)
+
+OBJS := $(SRC:.cpp=.o)
+LIBS := $(addprefix -l,$(LIBS_NAME))
+CLEAN_LIST := $(TARGET) $(OBJS)
+
+# default rule
+default: all
+
+$(TARGET): $(OBJS)
+	$(CXX) $(OBJS) -o $(TARGET) $(LDFLAGS) $(LIBS)
+
+.PHONY: all
+all: $(TARGET)
+
+.PHONY: clean
+clean:
+	@echo CLEAN $(CLEAN_LIST)
+	@rm -f $(CLEAN_LIST)