def mlbox_counter():
from mlbox.preprocessing import Reader, Drift_thresholder
from mlbox.optimisation import Optimiser
from mlbox.prediction import Predictor
target_name = '601'
rd = Reader(sep=",")
df = rd.train_test_split(['train_egg.csv', 'test_egg.csv'], target_name)
# print(df)
dft = Drift_thresholder()
df = dft.fit_transform(df) # removing non-stable features (like ID,...)
opt = Optimiser(scoring="accuracy", n_folds=10)
space = {
'est__strategy': {
"search": "choice",
"space": ["LightGBM"]
},
'est__n_estimators': {
"search": "choice",
"space": [150]
},
'est__colsample_bytree': {
"search": "uniform",
"space": [0.8, 0.95]
},
'est__subsample': {
"search": "uniform",
"space": [0.8, 0.95]
},
'est__max_depth': {
"search": "choice",
"space": [5, 6, 7, 8, 9]
},
'est__learning_rate': {
"search": "choice",
"space": [0.07]
}
}
best = opt.optimise(space, df, 15)
prd = Predictor()
prd.fit_predict(best, df)
paths = ["train_regression.csv", "test_regression.csv"]
# Name of the feature to predict.
# This columns should only be present in the train set.
target_name = "SalePrice"
# Reading and cleaning all files
# Declare a reader for csv files
rd = Reader(sep=',')
# Return a dictionnary containing three entries
# dict["train"] contains training samples withtout target columns
# dict["test"] contains testing elements withtout target columns
# dict["target"] contains target columns for training samples.
dict = rd.train_test_split(paths, target_name)
dft = Drift_thresholder()
dict = dft.fit_transform(dict)
# Tuning
mape = make_scorer(lambda y_true, y_pred: 100 * np.sum(
np.abs(y_true - y_pred) / y_true) / len(y_true),
greater_is_better=False,
needs_proba=False)
# Declare an optimiser. You can declare your own score
# as presented here or use one in
# {"mean_absolute_error", "mean_squared_error","median_absolute_error","r2"}
opt = Optimiser(scoring=mape, n_folds=3)
opt.evaluate(None, dict)
# Space of hyperparameters
# The keys must respect the following syntax : "enc__param".
# "enc" = "ne" for na encoder
paths = ["train_classification.csv", "test_classification.csv"]
# Name of the feature to predict.
# This columns should only be present in the train set.
target_name = "Survived"
# Reading and cleaning all files
# Declare a reader for csv files
rd = Reader(sep=',')
# Return a dictionnary containing three entries
# dict["train"] contains training samples withtout target columns
# dict["test"] contains testing elements withtout target columns
# dict["target"] contains target columns for training samples.
data = rd.train_test_split(paths, target_name)
dft = Drift_thresholder()
data = dft.fit_transform(data)
# Tuning
# Declare an optimiser. Scoring possibilities for classification lie in :
# {"accuracy", "roc_auc", "f1", "neg_log_loss", "precision", "recall"}
opt = Optimiser(scoring='accuracy', n_folds=3)
opt.evaluate(None, data)
# Space of hyperparameters
# The keys must respect the following syntax : "enc__param".
# "enc" = "ne" for na encoder
# "enc" = "ce" for categorical encoder
# "enc" = "fs" for feature selector [OPTIONAL]
# "enc" = "stck"+str(i) to add layer n°i of meta-features [OPTIONAL]
# "enc" = "est" for the final estimator
# "param" : a correct associated parameter for each step.
def model_auto_mlbox( filepath= [ "train.csv", "test.csv" ],
colX=None, coly=None,
do="predict",
outfolder="aaserialize/",
model_type="regressor/classifier",
params={ "csv_seprator" : ",", "train_size" : 0.5, "score_metric" : "accuracy",
"n_folds": 3, "n_step": 10},
param_space = {
'est__strategy':{"search":"choice", "space":["LightGBM"]},
'est__n_estimators':{"search":"choice", "space":[150]},
'est__colsample_bytree':{"search":"uniform", "space":[0.8,0.95]},
'est__subsample':{"search":"uniform", "space":[0.8,0.95]},
'est__max_depth':{"search":"choice", "space":[5,6,7,8,9]},
'est__learning_rate':{"search":"choice", "space":[0.07]}
},
generation=1,
population_size=5,
verbosity=2,
):
"""
Using mlbox
https://www.analyticsvidhya.com/blog/2017/07/mlbox-library-automated-machine-learning/
Parameters
----------
df : TYPE
DESCRIPTION.
colX : TYPE
DESCRIPTION.
coly : TYPE
DESCRIPTION.
outfolder : TYPE, optional
DESCRIPTION. The default is "aaserialize/".
model_type : TYPE, optional
DESCRIPTION. The default is "regressor/classifier".
params : TYPE, optional
DESCRIPTION. The default is {"train_size" : 0.5}.
generation : TYPE, optional
DESCRIPTION. The default is 1.
population_size : TYPE, optional
DESCRIPTION. The default is 5.
verbosity : TYPE, optional
DESCRIPTION. The default is 2.
Returns
-------
None.
"""
from mlbox.preprocessing import Reader,Drift_thresholder
from mlbox.optimisation import Optimiser
from mlbox.prediction import Predictor
p = dict2(params)
## Pre-process
"""
df (dict, default = None) –
Dataset dictionary. Must contain keys and values:
”train”: pandas DataFrame for the train set.
”test” : pandas DataFrame for the test set.
”target” : encoded pandas Serie for the target on train set (with dtype=’float’ for a regression or dtype=’int’ for a classification). Indexes should match the train set.
"""
rd = Reader(sep = p.csv_separator)
df = rd.train_test_split( filepath, coly) # Reading and preprocessing (dates, ...)
dft = Drift_thresholder()
df = dft.fit_transform(df) # Removing non-stable features (like ID,...)
### Optimal parameter
# score_rmse = make_scorer(lambda y_true, y_pred: np.sqrt(np.sum((y_true - y_pred)**2)/len(y_true)), greater_is_better=False, needs_proba=False)
# opt = Optimiser(scoring = rmse, n_folds = 3)
opt = Optimiser(scoring = p.score_metric, n_folds = p.n_folds)
param_optim = opt.optimise(param_space, df, p.n_step)
if do == "prediction" :
clf = Predictor(to_path= outfolder, verbose=True)
#Fit and predict and save on disk
clf.fit_predict(param_optim, df)
# Load the predictions
preds = pd.read_csv("save/"+coly+"_predictions.csv")
print(preds.shape, preds.head(5))
"""