View perf2.r
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# Get performance measures | |
overall_accuracy_ols = cm_ols$overall['Accuracy'] |
View predict2.r
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# Predict the test set using the model | |
pred_ols = predict(lmmod, test[,3:32], type="response") | |
pred_ols | |
# Apply a threshold | |
new_pred_ols = ifelse(pred_ols >= 0.5, 1, 0) | |
new_pred_ols = data.frame(new_pred_ols) | |
data_ols = cbind(test[,2], new_pred_ols) | |
names(data_ols) = c("actual", "pred") | |
xtab_ols = table(data_ols$actual, data_ols$pred) |
View train model2.r
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# Train the model (Logistic regression) | |
lmmod = lm(diagnosis ~ . , data = train[,2:32]) | |
summary(lmmod) | |
coeftest(lmmod, vcov. = vcovHC, type = "HC1") |
View perf.r
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# Get performance measures | |
overall_accuracy_lasso = cm_lasso$overall['Accuracy'] |
View predict.r
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# Predict the test set using the model | |
pred_lasso = predict(glmmod, test_sparse, type="response", s=best.lambda) | |
pred_lasso | |
# Apply a threshold | |
new_pred_lasso = ifelse(pred_lasso >= 0.5, 1, 0) | |
new_pred_lasso = data.frame(new_pred_lasso) | |
data_lasso = cbind(test[,2], new_pred_lasso) | |
names(data_lasso) = c("actual", "pred") | |
xtab_lasso = table(data_lasso$actual, data_lasso$pred) |
View train model.r
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# Train the model | |
glmmod = glmnet(x=train_sparse, y=as.factor(train[,2]), alpha=1, family="binomial") | |
plot(glmmod, xvar="lambda") | |
glmmod | |
coef(glmmod)[,100] | |
# Try cross validation lasso | |
cv.glmmod = cv.glmnet(x=train_sparse, y=as.factor(train[,2]), alpha=1, family="binomial") | |
plot(cv.glmmod) |
View data prep.r
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library(Matrix) | |
library(glmnet) | |
library(pROC) | |
library(caret) | |
# Import dataset | |
data1 = read.csv(file = "./data/input/breast-cancer.csv") | |
data1$diagnosis<-ifelse(data1$diagnosis=='M', 1,0) | |
data2 = data.matrix(data1) | |
Matrix(data2, sparse = TRUE) |
View perform empath on text.py
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if __name__ == "__main__": | |
lexicon = Empath() | |
result = lexicon.analyze("the quick brown fox jumps over the lazy dog", normalize=True) | |
df0 = pd.Series(result, name = 'KeyValue') | |
logging.getLogger().setLevel(logging.INFO) | |
col_names = df0.keys() | |
df = pd.DataFrame(columns=col_names) | |
View data_prep.py
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# Load libraries | |
import os | |
import logging | |
from empath import Empath | |
import pandas as pd | |
# Set up folder locations | |
source_folder_path_list = [] | |
source_folder_path = "C:/Users/Marriane/Documents/GitHub/empath-on-movie-reviews/data/scale_whole_review.tar (with text)/scale_whole_review/scale_whole_review/" | |
folder_list = ["Dennis+Schwartz/txt.parag", "James+Berardinelli/txt.parag", "Scott+Renshaw/txt.parag", "Steve+Rhodes/txt.parag"] |
View adding more map plot functions.r
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# Using map_data() | |
worldmap <- map_data ("world") | |
mapplot1 <- ggplot(worldmap) + | |
geom_map(data = worldmap, map = worldmap, aes(x=long, y=lat, map_id=region), col = "white", fill = "gray50") + | |
geom_scatterpie(aes(x=longitude, y=latitude, group = country, r = multiplier*6), | |
data = final_data, cols = colnames(final_data[,c(2:11)])) + | |
xlim(-20,60) + ylim(10, 75) + | |
scale_fill_brewer(palette = "Paired") + | |
geom_text(aes(x=longitude, y=latitude, group = country, label = country), data = final_data, stat = "identity", |
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