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Version: 4.5

Log a sklearn Model

Train & Log a Custom Scikit-Learn Model with Katonic-SDK Log package.

Train and Log a Custom built Scikit-Learns Model with Katonic-SDK Log package.

Import necessary packages​

import os

import pandas as pd
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, roc_auc_score, log_loss, recall_score, f1_score, precision_score
from sklearn.linear_model import LogisticRegression
from katonic.log.logmodel import LogModel

Define Experiment name​

experiment_name= "sklearn_model"

Initiate LogModel with experiment name​

lm = LogModel(experiment_name, source_name="scikit_learn_logging.ipynb")

Check Metadata of the created / existing experiment​

# experiment id
exp_id = lm.id

print("experiment name: ", lm.name)
print("experiment location: ", lm.location)
print("experiment id: ", lm.id)
print("experiment status: ", lm.stage)

Artifact path where you want to log your model​

artifact_path = "scikit-learn-model"

Read data for training​

df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/diabetes.csv')
df.head()

Get Features and Labels​

x = df.drop(columns=['Outcome'], axis=1)
y = df['Outcome']

Split the dataset in Train and Test​

X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=.20,random_state=98)

Define Evaluation Metrics​

def metric(actual, pred):
    acc_score = accuracy_score(actual, pred)
    recall = recall_score(actual, pred)
    precision_scr = precision_score(actual, pred)
    f1_scr = f1_score(actual, pred)
    auc_roc = roc_auc_score(actual, pred)
    log_los = log_loss(actual, pred)

    return (
        acc_score,
        auc_roc,
        log_los,
        recall,
        f1_scr,
        precision_scr
    )

Train Random Forest Model​

model_clf = RandomForestClassifier(max_depth=2, random_state=0)
model_clf.fit(X_train, y_train)

Calculate metrics of the Random Forest model​

y_pred = model_clf.predict(X_test)
(acc_score, auc_roc, log_los, recall, f1_scr, precision_scr) = metric(y_test, y_pred)

model_mertics = {
    "accuracy_score": acc_score,
    "roc_auc_score": auc_roc,
    "log_loss": log_los,
    "recall": recall,
    "f1_score": f1_scr,
    "precision_score": precision_scr
}

Log Random Forest Model​

lm.model_logging(
    model_name="random_forest",
    model_type="scikit-learn",
    model=model_clf,
    artifact_path=artifact_path,
    current_working_dir=f'{os.getcwd()}/scikit_learn_logging.ipynb',
    metrics=model_mertics
)

Train Logistic Regression Model​

model_clf = LogisticRegression(random_state=0)
model_clf.fit(X_train, y_train)

Calculate metrics of the Logistic Regression model​

y_pred = model_clf.predict(X_test)
(acc_score, auc_roc, log_los, recall, f1_scr, precision_scr) = metric(y_test, y_pred)

model_mertics = {
    "accuracy_score": acc_score,
    "roc_auc_score": auc_roc,
    "log_loss": log_los,
    "recall": recall,
    "f1_score": f1_scr,
    "precision_score": precision_scr
}

Log Logistic Regression model​

Note: When you are logging models supported by scikit-learn, please use scikit-learn as model_type.

lm.model_logging(
    model_name="logistic_regression",
    model_type="scikit-learn",
    model=model_clf,
    artifact_path=artifact_path,
    current_working_dir=f'{os.getcwd()}/scikit_learn_logging.ipynb',
    metrics=model_mertics
)

Train Adaboost Model​

model_clf = AdaBoostClassifier(random_state=0)
model_clf.fit(X_train, y_train)

Calculate metrics of the Adaboost model​

y_pred = model_clf.predict(X_test)
(acc_score, auc_roc, log_los, recall, f1_scr, precision_scr) = metric(y_test, y_pred)

model_mertics = {
    "accuracy_score": acc_score,
    "roc_auc_score": auc_roc,
    "log_loss": log_los,
    "recall": recall,
    "f1_score": f1_scr,
    "precision_score": precision_scr
}

Log Adaboost model​

lm.model_logging(
    model_name="adaboostclassifier",
    model_type="scikit-learn",
    model=model_clf,
    artifact_path=artifact_path,
    current_working_dir=f'{os.getcwd()}/scikit_learn_logging.ipynb',
    metrics=model_mertics
)

Train Gradient Boost Model​

model_clf = GradientBoostingClassifier(random_state=0)
model_clf.fit(X_train, y_train)

Calculate metrics of the Gradient Boost model​

y_pred = model_clf.predict(X_test)
(acc_score, auc_roc, log_los, recall, f1_scr, precision_scr) = metric(y_test, y_pred)

model_mertics = {
    "accuracy_score": acc_score,
    "roc_auc_score": auc_roc,
    "log_loss": log_los,
    "recall": recall,
    "f1_score": f1_scr,
    "precision_score": precision_scr
}

Log Gradientboost model​

lm.model_logging(
    model_name="gradientboostclassifier",
    model_type="scikit-learn",
    model=model_clf,
    artifact_path=artifact_path,
    current_working_dir=f'{os.getcwd()}/scikit_learn_logging.ipynb',
    metrics=model_mertics
)

Check all the logged Experiments​

You can search and get all the logged experiments with experiment ID.

df_runs = lm.search_runs(exp_id)
print("Number of runs done : ", len(df_runs))
df_runs.head()