def fetch_df(step, output_name):
output_data = step.get_output_data(output_name)
download_path = './outputs/' + output_name
output_data.download(download_path)
df_path = get_download_path(download_path, output_name) + '/data'
return dprep.auto_read_file(path=df_path)
def load_azureml_df(local_cache_path=None, file_split=Split.TRAIN, file_type="txt"):
"""
Loads the SNLI dataset as AzureML dataflow object
Download the dataset from "https://nlp.stanford.edu/projects/snli/snli_1.0.zip", unzip,
and load.
Args:
local_cache_path (str): Path (directory or a zip file) to cache the downloaded zip file.
If None, all the intermediate files will be stored in a temporary directory and removed
after use.
file_split (str): File split to load. One of (dev, test, train)
file_type (str): File type to load. One of (txt, jsonl)
Returns:
AzureML dataflow: SNLI dataset
"""
with download_path(local_cache_path) as path:
filepath = os.path.join(path, "snli_1.0.zip")
snlipath = _maybe_download_and_extract(filepath, file_split, file_type)
# NOTE: this works for the txt format but not the jsonl format
df = dprep.auto_read_file(snlipath)
return df
def prepareDataForMLTraining(testSize, dataset_root, shouldShuffle):
# dataset_root path must be set to where YOU are storing the .csv file.
#Creates a DataFlow object from .csv file
dataFlow = dprep.auto_read_file(dataset_root, False)
#Defines the input (X) columns and the output-to-predict (Y) columns from the .csv file.
dataflow_X = dataFlow.keep_columns([
'Soil_Name', 'MEAN_Eleva', 'V.A.T(F)', 'R.A.T(F)', 'M.A.T(F)',
'V.PET(inch)', 'R.PET(inch)', 'M.PET(inch)', 'V.T.R(inch)',
'R.T.R(inch)'
])
dataflow_Y = dataFlow.keep_columns('NormalizedYield')
#Converts data into a pandas DataFrame to ease in splitting data into Train/Test sets.
x_dataFlow_Pandas = dataflow_X.to_pandas_dataframe()
y_dataFlow_Pandas = dataflow_Y.to_pandas_dataframe()
#Splits data into a train portion and a test portion
#User-defined parameters for how the dataset is split for training/testing purposes.
#Test size should be a float between 0 and 1. Train size is 1 - testSize.
#shouldShuffle determines whether data is given a final shuffle before division.
X_toTrain, X_toTest, Y_toTrain, Y_toTest = train_test_split(
x_dataFlow_Pandas,
y_dataFlow_Pandas,
test_size=testSize,
random_state=173,
shuffle=shouldShuffle)
return (X_toTrain, X_toTest, Y_toTrain, Y_toTest)
def test_fit_transform(self):
import azureml.dataprep as dprep
path = get_dataset('infert').as_filepath()
dflow = dprep.auto_read_file(path=path)
dprep_data = DprepDataStream(dflow)
file_data = FileDataStream.read_csv(path)
xf = MinMaxScaler(columns={'in': 'induced', 'sp': 'spontaneous'})
pipe = Pipeline([xf])
transformed_data = pipe.fit_transform(file_data)
transformed_data1 = pipe.fit_transform(dprep_data)
assert_array_equal(transformed_data.columns, transformed_data1.columns)
assert_2d_array_equal(transformed_data.values,
transformed_data1.values)
from azureml.core import Webservice
from azureml.core.webservice import AciWebservice
# try:
# setting the local env to hadnle missing packages
run_user_managed = RunConfiguration()
run_user_managed.environment.python.user_managed_dependencies = False
# Create workspace object for existing one and create an experiment
ws = Workspace.from_config('subscription.json')
print(ws.name, ws.location, ws.resource_group, ws.location, sep='\t')
experiment = Experiment(workspace=ws, name='experiment1')
# full path to training data,testing data
file_path1 = os.path.join(os.getcwd(), "cumodelwo2014.csv")
dflowtr = dprep.auto_read_file(path=file_path1)
file_path2 = os.path.join(os.getcwd(), "test2014.csv")
dflowte = dprep.auto_read_file(path=file_path2)
# Specifying x(causal) and y(response) attributes in training data
dflowtr_x = dflowtr.keep_columns([
'cell-ID', 'Soil_Name', 'MEAN_Yld_V', 'COUNT_Yld', 'MEAN_Eleva',
'RANGE_Elev', 'Crop-Type', 'V.A.T(F)', 'R.A.T(F)', 'M.A.T(F)',
'V.PET(inch)', 'R.PET(inch)', 'M.PET(inch)', 'V.T.R(inch)', 'R.T.R(inch)',
'M.T.R(inch)'
])
dflowtr_y = dflowtr.keep_columns('NormalizedYield')
# causal factors in training dataframe
trainingx_df = dflowtr_x.to_pandas_dataframe()
# response variable in training dataframe
trainingy_df = dflowtr_y.to_pandas_dataframe()
#***
## Part 2 - Data Ingestion
# %%
#Load the train and test files as Pandas dataframes...
df_train = pd.read_csv('./kaggle/input/train.csv')
df_train.shape
# %%
df_train.info()
# %%
df_test = pd.read_csv('./kaggle/input/test.csv')
df_test.shape
# %%
dataflow = dprep.auto_read_file('./kaggle/input/train.csv')
dataflow.head(10)
# %%
dataflow_test = dprep.auto_read_file('./kaggle/input/test.csv')
dataflow_test.head(10)
# %% [markdown]
# ***
## Part 3 - Feature Engineering I
#
#Feature engineering necessary to support exploratory data analysis (EDA) below.
#
#In this section a range of feature extraction actions are performed using Microsoft's ML Dataprep SDK for Python:
#1. Append the test data set to training data set so that feature engineering can be addressed collectively;
#2. Filling in empty values in "Cabin" and "Fare" where simple rules can be applied;
def main():
# local compute
run_user_managed = RunConfiguration()
run_user_managed.environment.python.user_managed_dependencies = False
# print to check azure sdk installation
print(azureml.core.VERSION)
# create workspace object to connect to omtest workspace in MLSERVICE
ws = Workspace.from_config('./config.json')
# default data store
# ds = ws.get_default_datastore()
# print(ds)
# choose a name for the run history container in the workspace
experiment_name = 'automated-ml-regression'
# project folder
project_folder = './automated-ml-regression'
output = {}
output['SDK version'] = azureml.core.VERSION
output['Subscription ID'] = ws.subscription_id
output['Workspace'] = ws.name
output['Resource Group'] = ws.resource_group
output['Location'] = ws.location
output['Project Directory'] = project_folder
pd.set_option('display.max_colwidth', -1)
pd.DataFrame(data=output, index=['']).T
# stats for all the columns
dflow = dprep.auto_read_file(path='/Users/omprakashnekkanti/Desktop/Spring 2019/CS445-Capstone/automatedML/cuformodel.csv')
print(type(dflow))
dflow.get_profile()
# filepath as a string
file_path = os.path.join(os.getcwd(), 'cuformodel.csv')
print(file_path)
print(type(file_path))
# dflow_prepared = dprep.Dataflow.open(file_path)
# dflow_prepared.get_profile()
dflow_X = dflow.keep_columns([
'cell-ID', 'Soil_Name', 'MEAN_Yld_V', 'COUNT_Yld', 'MEAN_Eleva',
'RANGE_Elev', 'Crop-Type', 'V.A.T(F)', 'R.A.T(F)', 'M.A.T(F)',
'V.PET(inch)', 'R.PET(inch)', 'M.PET(inch)', 'V.T.R(inch)', 'R.T.R(inch)',
'M.T.R(inch)'
])
dflow_y = dflow.keep_columns('NormalizedYield')
x_df = dflow_X.to_pandas_dataframe()
y_df = dflow_y.to_pandas_dataframe()
x_train, x_test, y_train, y_test = train_test_split(
x_df, y_df, test_size=0.2, random_state=223)
# flatten y_train to 1d array
y_train.values.flatten()
automl_settings = {
"iteration_timeout_minutes": 20,
"iterations": 40,
"primary_metric": 'mean_absolute_error',
"preprocess": False,
"verbosity": logging.INFO,
"n_cross_validations": 10
}
# local compute
automated_ml_config = AutoMLConfig(
task='regression',
debug_log='automated_ml_errors.log',
path=project_folder,
X=x_train.values,
y=y_train.values.flatten(),
**automl_settings)
experiment = Experiment(ws, experiment_name)
local_run = experiment.submit(automated_ml_config, show_output=True)
def to_dataflow(self) -> 'dprep.Dataflow':
import azureml.dataprep as dprep
return dprep.auto_read_file(self.get_url())
# COMMAND ----------
import azureml.dataprep as dprep
# COMMAND ----------
dataset_root = "https://dprepdata.blob.core.windows.net/demo"
green_path = "/".join([dataset_root, "green-small/*"])
yellow_path = "/".join([dataset_root, "yellow-small/*"])
green_df = dprep.read_csv(path=green_path,
header=dprep.PromoteHeadersMode.GROUPED)
# auto_read_file will automatically identify and parse the file type, and is useful if you don't know the file type
yellow_df = dprep.auto_read_file(path=yellow_path)
green_df.head(5)
yellow_df.head(5)
# COMMAND ----------
all_columns = dprep.ColumnSelector(term=".*", use_regex=True)
drop_if_all_null = [
all_columns,
dprep.ColumnRelationship(dprep.ColumnRelationship.ALL)
]
useful_columns = [
"cost", "distance", "dropoff_datetime", "dropoff_latitude",
"dropoff_longitude", "passengers", "pickup_datetime", "pickup_latitude",
"pickup_longitude", "store_forward", "vendor"
parser.add_argument("--d_ts_2", type=str, help="d_ts_2")
parser.add_argument("--output", type=str, help="output")
args = parser.parse_args()
# parse inputs
h_ts_1_dr = args.h_ts_1
h_ts_2_dr = args.h_ts_2
h_ts_3_dr = args.h_ts_3
d_ts_1_dr = args.d_ts_1
d_ts_2_dr = args.d_ts_2
# =======================
# azureml-dataprep-sdk.py
# =======================
h_ts_1_dflow = dprep.auto_read_file(h_ts_1_dr)
h_ts_2_dflow = dprep.auto_read_file(h_ts_2_dr)
h_ts_3_dflow = dprep.auto_read_file(h_ts_3_dr)
d_ts_1_dflow = dprep.auto_read_file(d_ts_1_dr)
d_ts_2_dflow = dprep.auto_read_file(d_ts_2_dr)
# Pivot data
h_ts_1_pivot_dflow = h_ts_1_dflow.pivot(
['NODE_ID'], 'MW',
azureml.dataprep.api.engineapi.typedefinitions.SummaryFunction.MAX,
['MYDATE', 'HOUR'])
h_ts_2_pivot_dflow = h_ts_2_dflow.pivot(
['NODE_ID'], 'MW',
azureml.dataprep.api.engineapi.typedefinitions.SummaryFunction.MAX,
['MYDATE', 'HOUR'])
