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ff4fdac | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 | import argparse
import pandas as pd
import time
import mlflow
from mlflow.models.signature import infer_signature
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, FunctionTransformer, OneHotEncoder
from sklearn.compose import ColumnTransformer
from sklearn.ensemble import RandomForestClassifier
from sklearn.pipeline import Pipeline
if __name__ == "__main__":
### MLFLOW Experiment setup
experiment_name="appointment_cancellation_detector"
mlflow.set_experiment(experiment_name)
experiment = mlflow.get_experiment_by_name(experiment_name)
client = mlflow.tracking.MlflowClient()
run = client.create_run(experiment.experiment_id)
print("training model...")
# Time execution
start_time = time.time()
# Call mlflow autolog
mlflow.sklearn.autolog(log_models=False) # We won't log models right away
# Parse arguments given in shell script
parser = argparse.ArgumentParser()
parser.add_argument("--n_estimators")
parser.add_argument("--min_samples_split")
args = parser.parse_args()
# Import dataset
df = pd.read_csv("https://full-stack-assets.s3.eu-west-3.amazonaws.com/Deployment/doctolib_simplified_dataset_01.csv")
# X, y split
X = df.iloc[:, 3:-1]
y = df.iloc[:, -1].apply(lambda x: 0 if x=="No" else 1)
# Train / test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2)
# Preprocessing
def date_processing(df):
df = df.copy()
## Transform datetime into a number
df["ScheduledDay"] = pd.to_datetime(df["ScheduledDay"], yearfirst=True, infer_datetime_format=True)
df["AppointmentDay"] = pd.to_datetime(df["AppointmentDay"], yearfirst=True, infer_datetime_format=True)
## Get the difference between scheduled day and appointment
df["time_difference_between_scheduled_and_appointment"] = (df["AppointmentDay"] - df["ScheduledDay"]).dt.days
## Remove redundant info
df = df.drop(["ScheduledDay", "AppointmentDay"], axis=1)
return df
date_preprocessor = FunctionTransformer(date_processing)
# Preprocessing
categorical_features = ["Gender", "Neighbourhood"] # Select all the columns containing strings
categorical_transformer = OneHotEncoder(drop='first', handle_unknown='error') # à retirer : sparse=False
numerical_feature_mask = ~X_train.columns.isin(["Gender", "Neighbourhood", "ScheduledDay","AppointmentDay"]) # Select all the columns containing anything else than strings
numerical_features = X_train.columns[numerical_feature_mask]
numerical_transformer = StandardScaler()
feature_preprocessor = ColumnTransformer(
transformers=[
("categorical_transformer", categorical_transformer, categorical_features),
("numerical_transformer", numerical_transformer, numerical_features)
]
)
# Pipeline
n_estimators = int(args.n_estimators)
min_samples_split=int(args.min_samples_split)
model = Pipeline(steps=[
("Dates_preprocessing", date_preprocessor),
('features_preprocessing', feature_preprocessor),
("Regressor",RandomForestClassifier(n_estimators=n_estimators, min_samples_split=min_samples_split))
])
# Log experiment to MLFlow
with mlflow.start_run(run_id = run.info.run_id) as run:
model.fit(X_train, y_train)
predictions = model.predict(X_train)
# Log model seperately to have more flexibility on setup
mlflow.sklearn.log_model(
sk_model=model,
artifact_path="appointment_cancellation_detector",
registered_model_name="appointment_cancellation_detector_RF",
signature=infer_signature(X_train, predictions)
)
print("...Done!")
print(f"---Total training time: {time.time()-start_time}") |