creating visuals for DAI

01-connect-to-DAI.R

# Load dai package
library(dai)

# Connect to Driverless AI instance
dai_uri = "http://your.instance.name:12345"
usr = "user_id"
pwd = "password"
dai.connect(uri = dai_uri, username = usr, password = pwd, force_version = FALSE)

02-upload-titanic-data.R

# Import data: pick one of these 2 methods below

# Upload datafile (change path to the location on your client machine):
titanic_all = dai.upload_dataset("data/titanic.csv")

# import from S3:
titanic_all = dai.create_dataset("s3://h2o-public-test-data/smalldata/titanic/titanic_expanded.csv")

03-split-titanic-data.R

# Split data
partitions = dai.split_dataset(dataset = titanic_all, 
                               output_name1 = "titanic_train", output_name2 = "titanic_test",
                               ratio = 0.8, seed = 107030, target = "survived")
titanic_train = partitions[[1]]
titanic_test = partitions[[2]]

04-create-titanic-model-555.R

titanic_model = dai.train(training_frame = titanic_train, testing_frame = titanic_test, 
                          target_col = "survived", is_classification = TRUE, is_timeseries = FALSE,
                          cols_to_drop = c("boat","body","home.dest"),
                          time = 4, accuracy = 4, interpretability = 5,
                          experiment_name = "titanic-enh-model-445",
                          enable_gpus = TRUE, seed = 75252,
                          config_overrides = "make_python_scoring_pipeline = 'off'")

05a-predict-titanic-test.R

file_path = dai.download_file(titanic_model$test_predictions_path, 
                              dest_path = "data/titanic_test_predictions.csv", 
                              force=TRUE)
preds = read.csv(file_path)

05b-predict-titanic-test.R

titanic_scored = predict(titanic_model, newdata = titanic_all, 
                      include_columns = c("survived","pclass","sex","embarked"), 
                      return_df = TRUE)

06a-rdc-titanic-all.R

library(ggplot2)
library(scales)
library(ggthemes)

ggplot(titanic_scored, aes(x=survived.No)) +
  geom_density(fill='grey', alpha = .7, trim=TRUE) +
  theme_tufte(base_size = 12, base_family = 'Palatino') + geom_rangeframe() +
  labs(y = NULL) +
  theme(legend.position = "bottom",
        axis.text.y = element_blank(),
        axis.ticks.y = element_blank())

06b-rdc-titanic-all.R

ggplot(titanic_scored, aes(x=survived.No, fill=survived)) +
  geom_density(alpha = .5, trim=TRUE) +
  theme_tufte(base_size = 12, base_family = 'Palatino') + geom_rangeframe() +
  labs(y = NULL) +
  theme(legend.position = "bottom",
        axis.text.y = element_blank(),
        axis.ticks.y = element_blank())

07-rdc-titanic-by-sex.R

ggplot(titanic_scored, aes(x=survived.No, fill=survived)) +
  geom_density(alpha = .7, trim=TRUE) +
  facet_wrap(~sex, ncol=1, scales = "free_y") +
  labs(y = NULL) +
  theme_tufte(ticks=TRUE) + geom_rangeframe() + 
  theme(legend.position = "bottom",
        axis.text.y = element_blank(),
        axis.ticks.y = element_blank())

08-rdc-titanic-by-embarked.R

ggplot(titanic_scored[titanic_scored$embarked!="",], aes(x=survived.No, fill=survived)) +
  geom_density(alpha = .7, trim=TRUE) +
  facet_wrap(~embarked, ncol=1, scales = "free_y") +
  labs(y = NULL) +
  theme_tufte(ticks=TRUE) + geom_rangeframe() + 
  theme(legend.position = "bottom",
        axis.text.y = element_blank(),
        axis.ticks.y = element_blank())