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import os
import time
import numpy
import pandas as pd
import torch
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import average_precision_score
from sklearn.model_selection import GridSearchCV, train_test_split
from concrete.ml.sklearn import RandomForestClassifier as ConcreteRandomForestClassifier
def train(dev_folder="./dev"):
# Download the data-sets
if not os.path.isfile("./files/titanic.csv"):
raise ValueError(
"no dataset"
)
current_dir = os.path.dirname(os.path.realpath(__file__))
data = pd.read_csv(os.path.join(current_dir, "files/titanic.csv"))
def encode_age(df):
df.Age = df.Age.fillna(-0.5)
bins = (-1, 0, 5, 12, 18, 25, 35, 60, 120)
categories = pd.cut(df.Age, bins, labels=False)
df.Age = categories
return df
def encode_fare(df):
df.Fare = df.Fare.fillna(-0.5)
bins = (-1, 0, 8, 15, 31, 1000)
categories = pd.cut(df.Fare, bins, labels=False)
df.Fare = categories
return df
def encode_df(df):
df = encode_age(df)
df = encode_fare(df)
sex_mapping = {"male": 0, "female": 1}
df = df.replace({"Sex": sex_mapping})
embark_mapping = {"S": 1, "C": 2, "Q": 3}
df = df.replace({"Embarked": embark_mapping})
df.Embarked = df.Embarked.fillna(0)
df["Company"] = 0
df.loc[(df["SibSp"] > 0), "Company"] = 1
df.loc[(df["Parch"] > 0), "Company"] = 2
df.loc[(df["SibSp"] > 0) & (df["Parch"] > 0), "Company"] = 3
df = df[
[
"PassengerId",
"Pclass",
"Sex",
"Age",
"Fare",
"Embarked",
"Company",
"Survived",
]
]
return df
train = encode_df(data)
X_all = train.drop(["Survived", "PassengerId"], axis=1)
y_all = train["Survived"]
num_test = 0.20
X_train, X_test, y_train, y_test = train_test_split(
X_all, y_all, test_size=num_test, random_state=23
)
# n_estimators = 50
# max_depth = 4
# n_bits = 6
# n_jobs_xgb = 1
# n_jobs_gridsearch = -1
# A gridsearch to find the best parameters
parameters = {
"n_bits": [6],
"max_depth": [4, 8],
"n_estimators": [30, 50],
"n_jobs": [-1],
}
concrete_clf = ConcreteRandomForestClassifier()
# concrete_clf.fit(X_train, y_train)
# concrete_predictions = concrete_clf.predict(X_test)
grid_search = GridSearchCV(concrete_clf, parameters, cv=3, n_jobs=-1, scoring="accuracy")
grid_search.fit(X_train, y_train)
# Check the accuracy of the best model
print(f"Best score: {grid_search.best_score_}")
# Check best hyper-parameters
print(f"Best parameters: {grid_search.best_params_}")
# Extract best model
best_model = grid_search.best_estimator_
assert isinstance(best_model, ConcreteRandomForestClassifier)
best_model.compile(X_train)
# Export the final model such that we can reuse it in a client/server environment
# Save the model to be pushed to a server later
from concrete.ml.deployment import FHEModelDev
fhe_api = FHEModelDev(dev_folder, best_model)
fhe_api.save()
if __name__ == "__main__":
train() |