class Model():
def __init__(self, is_infer_only=False):
self.config = Config()
self.logger = Logger(__name__)
self._model = self.load_model()
self._label_encoder = self.load_labelencoder()
def train(self, directory):
dm = DataManager(directory)
if not dm.validate():
raise UiPathUsageException("No valid data to run this pipeline.")
data_df = dm.get_data()
X = data_df[dm.get_feature_columns()].values
y = data_df[dm.get_target_column()].values
help_string = "Warning: You have retrained a model which was generated by a TPOT optimization pipeline.\
\nFor optimal results please run the TPOT optimization pipeline from scratch by training package version [1.0]."
if not self.is_trained() or self.config.warm_start == True:
self._model = self.build_model(X, y)
else:
self._model.fit(X, y)
self.logger.info(f"Finished retraining model.")
self.logger.info(help_string)
joblib.dump(self._model,
os.path.join(self.config.cur_dir, "model", "Model.sav"))
def evaluate(self, evaluation_directory):
dm = DataManager(evaluation_directory)
data_df = dm.get_data()
if not dm.validate(for_train=False):
self.logger.info(
"No valid test data to run this evaluation pipeline.")
data_df = dm.get_data()
X = data_df[dm.get_feature_columns()].values
y = data_df[dm.get_target_column()].values
if not self.is_trained():
self.logger.info(_UNTRAINED_HELP)
else:
score = self._model.score(X, y)
self.logger.info(f"Evaluation score = {score}")
return score
def process_data(self, directory):
if not self.config.test_data_from_ui:
dm = DataManager(directory)
if not dm.validate():
raise UiPathUsageException(
"No valid data to run this pipeline.")
all_data = dm.get_data()
# Stratified split
percentage = self.config.process_data_split_percentage
train, test = train_test_split(
all_data,
test_size=percentage,
random_state=self.config.seed,
)
# Write train.csv
DataManager.write_dataframe(train, 'train',
self.config.train_data_directory)
# Write evaluate.csv
DataManager.write_dataframe(test, 'test',
self.config.test_data_directory)
else:
dm = DataManager(directory)
if not dm.validate():
raise UiPathUsageException(
"No valid data to run this pipeline.")
all_data = dm.get_data()
# Write train.csv
DataManager.write_dataframe(all_data, 'train',
self.config.train_data_directory)
self.logger.info(
"Did not split data into train and test sets. Model will be evaluated on data selected from UI."
)
def build_model(self, X, y):
# Perform missing value imputation as scikit-learn models can't handle NaN's
nan_imputer = SimpleImputer(missing_values=np.nan, strategy="mean")
X = nan_imputer.fit_transform(X)
pipeline_optimizer = TPOTRegressor(
generations=self.config.generations,
population_size=self.config.population_size,
offspring_size=self.config.offspring_size,
mutation_rate=self.config.mutation_rate,
crossover_rate=self.config.crossover_rate,
scoring=self.config.scoring,
cv=self.config.cv,
subsample=self.config.subsample,
n_jobs=-1,
max_time_mins=self.config.max_time_mins,
max_eval_time_mins=self.config.max_eval_time_mins,
random_state=self.config.seed,
config_dict=self.config.classifier_config_dict,
warm_start=self.config.warm_start,
memory=self.config.artifacts_directory,
verbosity=1)
# Fit TPOT to data
pipeline_optimizer.fit(X, y)
self.logger.info(f"Finished running TPOT optimization pipeline.")
# Export fitted pipeline to artifacts directory
pipeline_path = os.path.join(self.config.artifacts_directory,
"TPOT_pipeline.py")
pipeline_optimizer.export(pipeline_path)
self.logger.info(f"Saving best pipeline to {pipeline_path}")
# Create new pipeline which contains nan_imputer
pipe = Pipeline([
("nan_imputer", nan_imputer),
("tpot_pipeline", pipeline_optimizer.fitted_pipeline_),
])
return pipe
def predict(self, mlskill_input):
data = pd.read_json(mlskill_input)
predictions = self._model.predict(data.values)
return json.dumps(predictions.tolist())
def load_model(self):
if os.path.isfile(
os.path.join(self.config.cur_dir, "model", "Model.sav")):
self.logger.info(f"Loading pre-trained model...")
return joblib.load(
os.path.join(self.config.cur_dir, "model", "Model.sav"))
else:
return None
def load_labelencoder(self):
if os.path.isfile(
os.path.join(self.config.cur_dir, "model",
"LabelEncoder.sav")):
self.logger.info(f"Loading label encoder...")
return joblib.load(
os.path.join(self.config.cur_dir, "model", "LabelEncoder.sav"))
else:
return None
def is_trained(self):
if self._model is None:
return False
else:
return True
class DataManager():
def __init__(self, directory):
self.config = Config()
self.logger = Logger(__name__)
self.logger.info(f"Loading data from {directory}...")
self.target_column_name = self.config.target_column
self.feature_column_names = None
self._label_encoder = self.load_labelencoder()
self.is_single_file = self.config.csv_name is not None
self.raw_data = self.read_all_data(directory)
if self.raw_data is None: return
nclasses = self.num_classes()
self.logger.info(
f"Done read [{len(self.raw_data)}] points with [{nclasses}] classes."
)
if nclasses == 1:
self.logger.info("Data must have at least 2 classes.")
self.raw_data = None
def read_all_data(self, directory):
dataframe_from_csv = self.read_all_csv(directory)
if self.is_single_file:
if dataframe_from_csv is None:
self.logger.info(
f"Not able to read any valid csv data in [{os.path.join(directory, self.config.csv_name)}]"
)
return None
return dataframe_from_csv
else:
if dataframe_from_csv is None:
self.logger.info(
f"Unable to read any valid data from *.csv files in [{directory}]"
)
return None
if dataframe_from_csv[self.target_column_name].dtype == 'object':
label = dataframe_from_csv.loc[:, self.target_column_name]
dataframe_from_csv.loc[:, self.
target_column_name] = self._label_encoder.fit_transform(
label)
joblib.dump(
self._label_encoder,
os.path.join(self.config.cur_dir, "model", 'LabelEncoder.sav'))
self.logger.info(
f"Encoded label column ['{self.target_column_name}']")
return dataframe_from_csv
def read_all_csv(self, directory):
help_string = " The csv file must contain a header, a target column and and at least one feature column." \
" The target column name is set by the <input_column> variable of this run. The default value is 'target'."
paths = []
if self.is_single_file:
paths = [os.path.join(directory, self.config.csv_name)]
else:
paths = glob.glob(os.path.join(directory, "*.csv"), recursive=True)
frames = []
for path in paths:
try:
self.logger.verbose(
f"Attempting to read data from csv [{path}]"
f" with delimiter [{self.config.delimiter}]")
frame = pd.read_csv(path,
error_bad_lines=False,
delimiter=self.config.delimiter,
encoding=self.config.encoding)
except Exception as e:
self.logger.info(
f"Failed to read csv [{path}] exception:\n{e}")
continue
if self.target_column_name not in frame.columns:
self.logger.info(
f"File [{path}] does not have name [{self.target_column_name}] in header"
f"{list(frame.columns)}', skipping this file." +
help_string)
continue
frames.append(frame)
self.logger.verbose(
f"Read [{len(frame)}] data points from [{path}]\n")
if len(frames) == 0: return None
coalesced = reduce(lambda a, b: a.append(b), frames[1:], frames[0])
return coalesced
def validate(self, for_train=True):
if self.raw_data is None: return False
if not for_train: return True
# Validates if all classes have at least some number of minium training examples
min_count_per_class = int(
len(self.get_data()) * self.config.class_percentage_validation)
target_value_counts = self.get_data()[
self.target_column_name].value_counts()
not_enough_examples = target_value_counts[
target_value_counts < min_count_per_class]
if len(not_enough_examples) == 0: return True
help_string = (
f"Provided data does not have enough training examples to train,"
f"you must provide at least {min_count_per_class} training examples of each class"
)
for class_name, number_examples in not_enough_examples.iteritems():
self.logger.info(
f"Class [{class_name}] has [{number_examples}] data points.")
self.logger.info(help_string)
return False
@staticmethod
def write_dataframe(frame, name, directory=None):
config = Config()
if directory is None:
path = os.path.join(config.artifacts_directory, f"{name}.csv")
else:
path = os.path.join(directory, f"{name}.csv")
frame.to_csv(path, index=False, sep=config.delimiter)
checksum = DataManager.checksum(path)
return checksum
@staticmethod
def checksum(path):
hasher = hashlib.md5()
with open(path, 'rb') as infile:
hasher.update(infile.read())
return hasher.hexdigest()
def get_classes(self):
return self.raw_data[self.target_column_name].unique()
def num_classes(self):
return len(self.get_classes())
def get_data(self):
return self.raw_data
def get_target_column(self):
return self.target_column_name
def get_feature_columns(self):
self.feature_column_names = list(
self.raw_data.drop(self.target_column_name, axis=1).columns)
return self.feature_column_names
def load_labelencoder(self):
if os.path.isfile(
os.path.join(self.config.cur_dir, "model",
"LabelEncoder.sav")):
self.logger.info(f"Loading label encoder...")
return joblib.load(
os.path.join(self.config.cur_dir, "model", "LabelEncoder.sav"))
else:
return LabelEncoder()
class DataManager():
def __init__(self, directory):
self.config = Config()
self.logger = Logger(__name__)
self.logger.info(f"Loading data from {directory}...")
self.target_column_name = self.config.target_column
self.feature_column_names = None
self.is_single_file = self.config.csv_name is not None
self.raw_data = self.read_all_data(directory)
if self.raw_data is None: return
self.logger.info(f"Done read [{len(self.raw_data)}] points.")
def read_all_data(self, directory):
dataframe_from_csv = self.read_all_csv(directory)
if self.is_single_file:
if dataframe_from_csv is None:
self.logger.info(
f"Not able to read any valid csv data in [{os.path.join(directory, self.config.csv_name)}]"
)
return None
return dataframe_from_csv
else:
if dataframe_from_csv is None:
self.logger.info(
f"Unable to read any valid data from *.csv files in [{directory}]"
)
return None
return dataframe_from_csv
def read_all_csv(self, directory):
help_string = " The csv file must contain a header, a target column and and at least one feature column." \
" The target column name is set by the <input_column> variable of this run. The default value is 'target'."
paths = []
if self.is_single_file:
paths = [os.path.join(directory, self.config.csv_name)]
else:
paths = glob.glob(os.path.join(directory, "*.csv"), recursive=True)
frames = []
for path in paths:
try:
self.logger.verbose(
f"Attempting to read data from csv [{path}]"
f" with delimiter [{self.config.delimiter}]")
frame = pd.read_csv(path,
error_bad_lines=False,
delimiter=self.config.delimiter,
encoding=self.config.encoding)
except Exception as e:
self.logger.info(
f"Failed to read csv [{path}] exception:\n{e}")
continue
if self.target_column_name not in frame.columns:
self.logger.info(
f"File [{path}] does not have name [{self.target_column_name}] in header"
f"{list(frame.columns)}', skipping this file." +
help_string)
continue
frames.append(frame)
self.logger.verbose(
f"Read [{len(frame)}] data points from [{path}]\n")
if len(frames) == 0: return None
coalesced = reduce(lambda a, b: a.append(b), frames[1:], frames[0])
return coalesced
def validate(self, for_train=True):
if self.raw_data is None:
return False
elif not for_train:
return True
else:
return True
@staticmethod
def write_dataframe(frame, name, directory=None):
config = Config()
if directory is None:
path = os.path.join(config.artifacts_directory, f"{name}.csv")
else:
path = os.path.join(directory, f"{name}.csv")
frame.to_csv(path, index=False, sep=config.delimiter)
checksum = DataManager.checksum(path)
return checksum
@staticmethod
def checksum(path):
hasher = hashlib.md5()
with open(path, 'rb') as infile:
hasher.update(infile.read())
return hasher.hexdigest()
def get_data(self):
return self.raw_data
def get_target_column(self):
return self.target_column_name
def get_feature_columns(self):
self.feature_column_names = list(
self.raw_data.drop(self.target_column_name, axis=1).columns)
return self.feature_column_names