Reviews3 = makeTrainingModel(ReviewsKey, target, knobs, drop=dropped)
dropped = ['Score', 'ProfileName', 'Time', 'Description']
knobs = [6, 2, 7]
Reviews4 = makeTrainingModel(ReviewsKey, target, knobs, drop=dropped)
dropped = ['Summary', 'Score', 'ProfileName']
knobs = [6, 2, 7]
Reviews5 = makeTrainingModel(ReviewsKey, target, knobs, drop=dropped)
### Cannabis Time Series
##
## 4/2/6, seed=212507590 120 days 0 gap : 7, 7, 7 minutes
## 4 / 2 / 6 , 28 days, 1 gap 7 minutes
experiment = h2oai.start_experiment_sync(dataset_key=train_dai.key,
testset_key = test_dai.key,
target_col="Weekly_Sales",
is_classification=False,
cols_to_drop = ["sample_weight"],
accuracy=5,
time=3,
interpretability=1,
scorer="RMSE",
enable_gpus=True,
seed=1234,
time_col = "Date",
time_groups_columns = ["Store", "Dept"],
num_prediction_periods = 1,
num_gap_periods = 0)
train_key = h2oai.get_dataset_split_job(reviews_split_data).entity[0]
test_key = h2oai.get_dataset_split_job(reviews_split_data).entity[1]
# Reviews Default
dropped = [
'UserID', 'ProductId', 'Id', 'Summary', 'Score', 'HelpfulnessDenominator',
'HelpfulnessNumerator', 'ProfileName', 'Time'
]
knobs = [8, 2, 7]
reviews1 = h2oai.start_experiment_sync(experiment_name="Reviews NLP Big",
dataset_key=train_key,
testset_key=test_key,
target_col=target,
is_classification=True,
accuracy=knobs[0],
time=knobs[1],
interpretability=knobs[2],
enable_gpus=True,
cols_to_drop=dropped)
reviews1a = h2oai.start_experiment_sync(experiment_name="Reviews NLP Big TF",
dataset_key=train_key,
testset_key=test_key,
target_col=target,
is_classification=True,
accuracy=knobs[0],
time=knobs[1],
interpretability=knobs[2],
enable_gpus=True,
cols_to_drop=dropped,
def test_debug_pyclient():
from h2oai_client import Client
pd.set_option('display.max_rows', 50)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
# Login info
dai_url = "http://****:12345"
dai_user = "******"
dai_pwd = "****"
# Data Information
data_file_name = "****.csv"
y = "****"
# Transformers information
transformer_file_name = "****.py"
transformers_noncustom = []
transformers_custom_nontesting = []
# All Offical Transformers
transformers_noncustom = ['CVCatNumEncode', 'CVTargetEncode'
, 'CatOriginalTransformer', 'ClusterDistTransformer'
, 'ClusterIdTransformer', 'ClusterTETransformer', 'DatesTransformer'
, 'EwmaLagsTransformer', 'FrequentTransformer', 'InteractionsTransformer'
, 'IsHolidayTransformer', 'LagsAggregatesTransformer', 'LagsInteractionTransformer'
, 'LagsTransformer', 'LexiLabelEncoder', 'NumCatTETransformer', 'NumToCatTETransformer'
, 'NumToCatWoEMonotonicTransformer', 'NumToCatWoETransformer', 'OneHotEncodingTransformer'
, 'OriginalTransformer', 'SortedLETransformer', 'StrFeatureTransformer', 'TextClustDistTransformer'
, 'TextClustTETransformer', 'TextLinModelTransformer', 'TextTransformer', 'TruncSVDNumTransformer'
, 'WeightOfEvidenceTransformer']
# Any Installed Custom Transformers you don't want to test
transformers_custom_nontesting = ['MyLogTransformer']
all_nontest_transformers = transformers_noncustom + transformers_custom_nontesting
# STEP ZERO: Connect to Driverless AI
h2oai = Client(dai_url, dai_user, dai_pwd)
# STEP ONE: Load data set (and related tasks)
# view all data sets in DAI
all_data_sets = h2oai.list_datasets(0, 100)
all_data_sets = pd.DataFrame({
'key': list(map(lambda x: x.key, all_data_sets))
, 'name': list(map(lambda x: x.name, all_data_sets))})
print("PRE-LOADED DATASETS:")
print(all_data_sets)
# check if data was pre-loaded - if so use that data set - if not load data
if data_file_name in all_data_sets['name'].values:
print("\nData already loaded ", data_file_name)
data_key = all_data_sets[all_data_sets["name"] == data_file_name]["key"][0]
data_load_job = h2oai.get_dataset_job(data_key).entity
else:
print("\nLoading file ", data_file_name)
data_load_job = h2oai.upload_dataset_sync(data_file_name)
data_key = data_load_job.key
# STEP TWO: Load custom transformer (and related tasks)
# probably not good to just upload every time
# no function to delete from python, only from ssh-ing in
# rm tmp/contrib/transformers/[function]_randomletters_content.py
print("\nUploading Transformer ", transformer_file_name)
my_transformer = h2oai.upload_custom_recipe_sync(transformer_file_name)
# returns true or false - exit if fails - check DAI UI for error message
if my_transformer:
print("\nTransformer uploaded successfully\n")
else:
print("\nTransformer uploaded failed, exiting program.\n")
sys.exit()
# STEP THREE: Run experiment (and related tasks)
print("\nStarting Experiment\n")
experiment = h2oai.start_experiment_sync(
dataset_key=data_key
, target_col=y
, is_classification=True
, accuracy=1
, time=1
, interpretability=10
, scorer="F1"
, score_f_name=None
, config_overrides="""
feature_brain_level=0
exclude_transformers={dont_use}
""".format(dont_use=all_nontest_transformers)
)
# experiment = h2oai.get_model_job("lomotare").entity
# STEP FOUR: Check the transformation was used
# Download Summary
summary_path = h2oai.download(src_path=experiment.summary_path, dest_dir=".")
dir_path = "h2oai_experiment_summary_" + experiment.key
import zipfile
with zipfile.ZipFile(summary_path, 'r') as z:
z.extractall(dir_path)
# View Features
features = pd.read_table(dir_path + "/features.txt", sep=',', skipinitialspace=True)
print(features)
# STEP FIVE: Transform data and ensure it looks as expected
transform = h2oai.fit_transform_batch_sync(model_key=experiment.key
, training_dataset_key=data_key
, validation_dataset_key=None
, test_dataset_key=None
, validation_split_fraction=0.25
, seed=1234
, fold_column=None)
# Download the training and validation transformed data
transform_train_path = h2oai.download(src_path=transform.training_output_csv_path, dest_dir=".")
transform_validate_path = h2oai.download(src_path=transform.validation_output_csv_path, dest_dir=".")
transform_train = pd.read_table(transform_train_path, sep=',', skipinitialspace=True)
transform_validate = pd.read_table(transform_validate_path, sep=',', skipinitialspace=True)
print(transform_train.head())
print(transform_validate.head())
# STEP 1000: Clean up
os.remove(summary_path)
os.remove(transform_train_path)
os.remove(transform_validate_path)
shutil.rmtree(dir_path)
import h2oai_client
from h2oai_client import Client
h2oai = Client(address='http://129.213.63.69:12345',
username='******',
password='******')
train = h2oai.create_dataset_sync('/train.csv')
test = h2oai.create_dataset_sync('/test.csv')
experiment = h2oai.start_experiment_sync(dataset_key=train.key,
testset_key=test.key,
accuracy=10,
time=10,
interpretability=1,
is_classification=True,
target_col='LABEL',
is_timeseries=True,
time_col='DATE',
num_gap_periods=1,
num_prediction_periods=1)
print("Final Model Score on Validation Data: " +
str(round(experiment.valid_score, 3)))
print("Final Model Score on Test Data: " +
str(round(experiment.test_score, 3)))
, time_col = ""
, ratio = ratio
, seed = 1234
)
train_key = h2oai.get_dataset_split_job(card_split_data).entity[0]
test_key = h2oai.get_dataset_split_job(card_split_data).entity[1]
# Card Default
knobs = [6, 4, 6]
card_default = h2oai.start_experiment_sync(
experiment_name = "Card Default"
, dataset_key = train_key
, testset_key = test_key
, target_col = target
, is_classification = True
, accuracy = knobs[0]
, time = knobs[1]
, interpretability = knobs[2]
, enable_gpus = True
, cols_to_drop = dropped
)
# Card Monotonic
knobs = [6, 4, 7]
card_monotonic = h2oai.start_experiment_sync(
experiment_name = "Card Monotonic"
, dataset_key = train_key
, testset_key = test_key
, target_col = target
, is_classification = True
, accuracy = knobs[0]
time_col="",
ratio=ratio,
seed=1234)
train_key = h2oai.get_dataset_split_job(boston_split_data).entity[0]
test_key = h2oai.get_dataset_split_job(boston_split_data).entity[1]
dropped = []
# Housing Experiment #1
knobs = [7, 2, 8]
housing1 = h2oai.start_experiment_sync(experiment_name="Housing",
dataset_key=train_key,
testset_key=test_key,
target_col=target,
is_classification=False,
accuracy=knobs[0],
time=knobs[1],
interpretability=knobs[2],
scorer='RMSE',
enable_gpus=True,
cols_to_drop=dropped)
# Housing Experiment #2
knobs = [4, 2, 8]
housing2 = h2oai.start_experiment_sync(experiment_name="Housing Quick",
dataset_key=train_key,
testset_key=test_key,
target_col=target,
is_classification=False,
accuracy=knobs[0],
time=knobs[1],
fold_col="",
time_col="",
ratio=ratio,
seed=1234)
train_key = h2oai.get_dataset_split_job(diabetes_split_data).entity[0]
test_key = h2oai.get_dataset_split_job(diabetes_split_data).entity[1]
dropped = []
# Diabetes Default
knobs = [8, 2, 8]
diabetes1 = h2oai.start_experiment_sync(experiment_name="Diabetes",
dataset_key=train_key,
testset_key=test_key,
target_col=target,
is_classification=True,
accuracy=knobs[0],
time=knobs[1],
interpretability=knobs[2],
enable_gpus=True,
cols_to_drop=dropped)
# Diabetes GLM
diabetes2 = h2oai.start_experiment_sync(
experiment_name="Diabetes GLM",
dataset_key=train_key,
testset_key=test_key,
target_col=target,
is_classification=True,
accuracy=knobs[0],
time=knobs[1],
interpretability=knobs[2],
ratio=ratio,
seed=1234)
train_key = h2oai.get_dataset_split_job(titanic_split_data).entity[0]
test_key = h2oai.get_dataset_split_job(titanic_split_data).entity[1]
knobs = [8, 2, 8]
# Titanic Default
dropped = ['no.title', 'cabin', 'embarked', 'boat', 'body', 'home.dest']
titanic1 = h2oai.start_experiment_sync(experiment_name="Titanic",
dataset_key=train_key,
testset_key=test_key,
target_col=target,
is_classification=True,
accuracy=knobs[0],
time=knobs[1],
interpretability=knobs[2],
enable_gpus=True,
cols_to_drop=dropped)
# Titanic No Name
dropped = [
'name', 'no.title', 'cabin', 'embarked', 'boat', 'body', 'home.dest'
]
titanic2 = h2oai.start_experiment_sync(experiment_name="Titanic no Name",
dataset_key=train_key,
testset_key=test_key,
target_col=target,
is_classification=True,
accuracy=knobs[0],