diff --git a/Labs/API_Labs/FLASK_GCP_LAB/.gitignore b/Labs/API_Labs/FLASK_GCP_LAB/.gitignore index e69de29bb..139743217 100755 --- a/Labs/API_Labs/FLASK_GCP_LAB/.gitignore +++ b/Labs/API_Labs/FLASK_GCP_LAB/.gitignore @@ -0,0 +1 @@ +*.pkl diff --git a/Labs/API_Labs/FLASK_GCP_LAB/main.py b/Labs/API_Labs/FLASK_GCP_LAB/main.py new file mode 100644 index 000000000..a3180deef --- /dev/null +++ b/Labs/API_Labs/FLASK_GCP_LAB/main.py @@ -0,0 +1,42 @@ +from flask import Flask, request, jsonify +from predict import predict_iris +import os + +app = Flask(__name__) + +# Map numeric model output to human-readable class +label_map = { + 0: "setosa", + 1: "versicolor", + 2: "virginica" +} + +@app.route('/predict', methods=['POST']) +def predict(): + data = request.get_json() + + sepal_length = float(data['sepal_length']) + sepal_width = float(data['sepal_width']) + petal_length = float(data['petal_length']) + petal_width = float(data['petal_width']) + + print(sepal_length, sepal_width, petal_length, petal_width) + + # call model + prediction = predict_iris(sepal_length, sepal_width, petal_length, petal_width) + + # convert numeric class → label string for frontend + try: + pred_int = int(prediction) + pred_label = label_map.get(pred_int, str(pred_int)) + except Exception: + pred_label = str(prediction) + + return jsonify({'prediction': pred_label}) + +if __name__ == '__main__': + app.run( + debug=True, + host="0.0.0.0", + port=int(os.environ.get("PORT", 8080)) + ) diff --git a/Labs/API_Labs/FLASK_GCP_LAB/model/model.pkl b/Labs/API_Labs/FLASK_GCP_LAB/model/model.pkl index 6395f8f7d..57b835e0f 100755 Binary files a/Labs/API_Labs/FLASK_GCP_LAB/model/model.pkl and b/Labs/API_Labs/FLASK_GCP_LAB/model/model.pkl differ diff --git a/Labs/API_Labs/FLASK_GCP_LAB/predict.py b/Labs/API_Labs/FLASK_GCP_LAB/predict.py new file mode 100644 index 000000000..b2b701899 --- /dev/null +++ b/Labs/API_Labs/FLASK_GCP_LAB/predict.py @@ -0,0 +1,19 @@ +import numpy as np +import joblib +import os +from train import run_training + +# Load the trained model +model = joblib.load("model/model.pkl") + +def predict_iris(sepal_length, sepal_width, petal_length, petal_width): + input_data = np.array([[sepal_length, sepal_width, petal_length, petal_width]]) + prediction = model.predict(input_data) + return prediction[0] + +if __name__ == "__main__": + if os.path.exists("model/model.pkl"): + print("Model loaded successfully") + else: + os.makedirs("model", exist_ok=True) + run_training() diff --git a/Labs/API_Labs/FLASK_GCP_LAB/src/main.py b/Labs/API_Labs/FLASK_GCP_LAB/src/main.py index c465f2bed..a3180deef 100755 --- a/Labs/API_Labs/FLASK_GCP_LAB/src/main.py +++ b/Labs/API_Labs/FLASK_GCP_LAB/src/main.py @@ -4,18 +4,39 @@ app = Flask(__name__) +# Map numeric model output to human-readable class +label_map = { + 0: "setosa", + 1: "versicolor", + 2: "virginica" +} + @app.route('/predict', methods=['POST']) def predict(): - data = request.get_json() # Get data as JSON + data = request.get_json() + sepal_length = float(data['sepal_length']) - sepal_width = float(data['sepal_width']) + sepal_width = float(data['sepal_width']) petal_length = float(data['petal_length']) - petal_width = float(data['petal_width']) + petal_width = float(data['petal_width']) print(sepal_length, sepal_width, petal_length, petal_width) + # call model prediction = predict_iris(sepal_length, sepal_width, petal_length, petal_width) - return jsonify({'prediction': prediction}) + + # convert numeric class → label string for frontend + try: + pred_int = int(prediction) + pred_label = label_map.get(pred_int, str(pred_int)) + except Exception: + pred_label = str(prediction) + + return jsonify({'prediction': pred_label}) if __name__ == '__main__': - app.run(debug=True, host="0.0.0.0", port=int(os.environ.get("PORT", 8080))) + app.run( + debug=True, + host="0.0.0.0", + port=int(os.environ.get("PORT", 8080)) + ) diff --git a/Labs/API_Labs/FLASK_GCP_LAB/src/model/model.pkl b/Labs/API_Labs/FLASK_GCP_LAB/src/model/model.pkl new file mode 100644 index 000000000..0fa3712b4 Binary files /dev/null and b/Labs/API_Labs/FLASK_GCP_LAB/src/model/model.pkl differ diff --git a/Labs/API_Labs/FLASK_GCP_LAB/src/test_api.py b/Labs/API_Labs/FLASK_GCP_LAB/src/test_api.py index e278713e1..dffe23bcf 100755 --- a/Labs/API_Labs/FLASK_GCP_LAB/src/test_api.py +++ b/Labs/API_Labs/FLASK_GCP_LAB/src/test_api.py @@ -1,18 +1,29 @@ import requests +import json -url = 'http://127.0.0.1:5000/predict' +url = 'http://127.0.0.1:8080/predict' -data = { +payload = { 'sepal_length': 5.1, 'sepal_width': 3.5, 'petal_length': 1.4, 'petal_width': 0.2 } -response = requests.post(url, data=data) +headers = { + 'Content-Type': 'application/json' +} + +response = requests.post(url, data=json.dumps(payload), headers=headers) + +print("Status:", response.status_code) +print("Body:", response.text) if response.status_code == 200: - prediction = response.json()['prediction'] - print('Predicted species:', prediction) + try: + prediction = response.json()['prediction'] + print('Predicted species:', prediction) + except Exception as e: + print("Could not parse JSON:", e) else: - print('Error:', response.status_code) \ No newline at end of file + print('Error:', response.status_code) diff --git a/Labs/API_Labs/FLASK_GCP_LAB/src/train.py b/Labs/API_Labs/FLASK_GCP_LAB/src/train.py index 894d3808f..f556b5023 100755 --- a/Labs/API_Labs/FLASK_GCP_LAB/src/train.py +++ b/Labs/API_Labs/FLASK_GCP_LAB/src/train.py @@ -1,34 +1,40 @@ -import joblib import os -import pandas as pd -from sklearn.linear_model import LogisticRegression +import joblib +import numpy as np +from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split +from sklearn.ensemble import RandomForestClassifier +from sklearn.metrics import accuracy_score +def run_training(): + # 1. Load dataset (Iris) + iris = load_iris() + X = iris.data # shape (150, 4) + y = iris.target # 0,1,2 classes -def run_training(): - """ - Train the model - """ - # Read the training data - dataset = pd.read_csv('data/IRIS.csv') - - # Split into labels and targets - X = dataset.drop("species", axis=1).copy() - y = dataset["species"].copy() - - # Create train and test set + # 2. Train / test split (for sanity) X_train, X_test, y_train, y_test = train_test_split( - X, y, test_size=0.25, random_state=26) + X, y, test_size=0.2, random_state=42, stratify=y + ) - # Training the model - model = LogisticRegression(random_state=26) + # 3. Train a model + model = RandomForestClassifier( + n_estimators=100, + random_state=42 + ) model.fit(X_train, y_train) - model.feature_names = X.columns - # Persist the trained model - if not os.path.exists("../model"): - os.makedirs("../model") - joblib.dump(model, "../model/model.pkl") + # 4. Quick eval just so we know it learned + y_pred = model.predict(X_test) + acc = accuracy_score(y_test, y_pred) + print(f"Model accuracy: {acc:.3f}") + + # 5. Make sure model/ directory exists + os.makedirs("model", exist_ok=True) + + # 6. Save model to model/model.pkl + joblib.dump(model, "model/model.pkl") + print("Saved trained model to model/model.pkl") if __name__ == "__main__": run_training() diff --git a/Labs/API_Labs/FLASK_GCP_LAB/streamlit_app.py b/Labs/API_Labs/FLASK_GCP_LAB/streamlit_app.py index 868bcab3f..72aa33f34 100755 --- a/Labs/API_Labs/FLASK_GCP_LAB/streamlit_app.py +++ b/Labs/API_Labs/FLASK_GCP_LAB/streamlit_app.py @@ -1,27 +1,105 @@ import streamlit as st +import pandas as pd import requests -import os - -st.title('IRIS Prediction') - -sepal_length = st.number_input('Sepal Length', min_value=0.0, max_value=10.0, step=0.1) -sepal_width = st.number_input('Sepal Width', min_value=0.0, max_value=10.0, step=0.1) -petal_length = st.number_input('Petal Length', min_value=0.0, max_value=10.0, step=0.1) -petal_width = st.number_input('Petal Width', min_value=0.0, max_value=10.0, step=0.1) - -if st.button('Predict'): - data = { - 'sepal_length': sepal_length, - 'sepal_width': sepal_width, - 'petal_length': petal_length, - 'petal_width': petal_width - } - try: - response = requests.post('https://iris-app-155173250771.us-central1.run.app/predict', json=data) - if response.status_code == 200: - prediction = response.json()['prediction'] - st.success(f'Predicted species: {prediction}') - else: - st.error(f'Error occurred during prediction. Status code: {response.status_code}') - except requests.exceptions.RequestException as e: - st.error(f'Error occurred during prediction: {str(e)}') +import json +from io import BytesIO + +st.set_page_config(page_title="Iris Species Predictor", page_icon="🌸", layout="centered") +st.title("🌸 Iris Flower Species Prediction App") + +PREDICT_URL = "http://127.0.0.1:8080/predict" # your local Flask API +headers = {"Content-Type": "application/json"} + +st.markdown( + """ + This app connects to a Flask API that predicts the **Iris flower species** + based on sepal and petal dimensions. + You can try single predictions or upload a CSV for batch results. + """ +) + +option = st.sidebar.radio("Select Mode:", ["Single Prediction", "Batch Prediction (CSV)"]) + +if option == "Single Prediction": + st.subheader("🔹 Enter Flower Measurements") + + sepal_length = st.number_input("Sepal Length (cm)", min_value=0.0, max_value=10.0, step=0.1) + sepal_width = st.number_input("Sepal Width (cm)", min_value=0.0, max_value=10.0, step=0.1) + petal_length = st.number_input("Petal Length (cm)", min_value=0.0, max_value=10.0, step=0.1) + petal_width = st.number_input("Petal Width (cm)", min_value=0.0, max_value=10.0, step=0.1) + + if st.button("Predict"): + payload = { + "sepal_length": sepal_length, + "sepal_width": sepal_width, + "petal_length": petal_length, + "petal_width": petal_width, + } + + try: + response = requests.post(PREDICT_URL, data=json.dumps(payload), headers=headers) + + if response.status_code == 200: + prediction = response.json()["prediction"] + st.success(f"🌼 Predicted species: **{prediction}**") + else: + st.error(f"❌ API Error: Status code {response.status_code}") + + except requests.exceptions.RequestException as e: + st.error(f"⚠️ Connection error: {str(e)}") + +else: + st.subheader("📂 Upload CSV for Batch Prediction") + + st.markdown( + """ + **Required columns:** + `sepal_length`, `sepal_width`, `petal_length`, `petal_width` + """ + ) + + uploaded_file = st.file_uploader("Choose a CSV file", type=["csv"]) + + if uploaded_file is not None: + df = pd.read_csv(uploaded_file) + st.write("✅ Uploaded data preview:") + st.dataframe(df.head()) + + if st.button("Run Batch Predictions"): + predictions = [] + for _, row in df.iterrows(): + payload = { + "sepal_length": row["sepal_length"], + "sepal_width": row["sepal_width"], + "petal_length": row["petal_length"], + "petal_width": row["petal_width"], + } + + try: + resp = requests.post(PREDICT_URL, data=json.dumps(payload), headers=headers) + if resp.status_code == 200: + pred = resp.json().get("prediction", "Error") + else: + pred = f"Error {resp.status_code}" + except Exception as e: + pred = f"Error: {str(e)}" + + predictions.append(pred) + + df["prediction"] = predictions + st.success("🎉 Batch prediction completed!") + st.dataframe(df) + + # Download results as CSV + output = BytesIO() + df.to_csv(output, index=False) + output.seek(0) + st.download_button( + label="⬇️ Download Predictions as CSV", + data=output, + file_name="iris_predictions.csv", + mime="text/csv", + ) + +st.markdown("---") +st.caption("Developed by Shivie Saksenaa | MLOps Flask + Streamlit Lab") diff --git a/Labs/API_Labs/FLASK_GCP_LAB/test_api.py b/Labs/API_Labs/FLASK_GCP_LAB/test_api.py new file mode 100644 index 000000000..dffe23bcf --- /dev/null +++ b/Labs/API_Labs/FLASK_GCP_LAB/test_api.py @@ -0,0 +1,29 @@ +import requests +import json + +url = 'http://127.0.0.1:8080/predict' + +payload = { + 'sepal_length': 5.1, + 'sepal_width': 3.5, + 'petal_length': 1.4, + 'petal_width': 0.2 +} + +headers = { + 'Content-Type': 'application/json' +} + +response = requests.post(url, data=json.dumps(payload), headers=headers) + +print("Status:", response.status_code) +print("Body:", response.text) + +if response.status_code == 200: + try: + prediction = response.json()['prediction'] + print('Predicted species:', prediction) + except Exception as e: + print("Could not parse JSON:", e) +else: + print('Error:', response.status_code) diff --git a/Labs/API_Labs/FLASK_GCP_LAB/train.py b/Labs/API_Labs/FLASK_GCP_LAB/train.py new file mode 100644 index 000000000..f556b5023 --- /dev/null +++ b/Labs/API_Labs/FLASK_GCP_LAB/train.py @@ -0,0 +1,40 @@ +import os +import joblib +import numpy as np +from sklearn.datasets import load_iris +from sklearn.model_selection import train_test_split +from sklearn.ensemble import RandomForestClassifier +from sklearn.metrics import accuracy_score + +def run_training(): + # 1. Load dataset (Iris) + iris = load_iris() + X = iris.data # shape (150, 4) + y = iris.target # 0,1,2 classes + + # 2. Train / test split (for sanity) + X_train, X_test, y_train, y_test = train_test_split( + X, y, test_size=0.2, random_state=42, stratify=y + ) + + # 3. Train a model + model = RandomForestClassifier( + n_estimators=100, + random_state=42 + ) + model.fit(X_train, y_train) + + # 4. Quick eval just so we know it learned + y_pred = model.predict(X_test) + acc = accuracy_score(y_test, y_pred) + print(f"Model accuracy: {acc:.3f}") + + # 5. Make sure model/ directory exists + os.makedirs("model", exist_ok=True) + + # 6. Save model to model/model.pkl + joblib.dump(model, "model/model.pkl") + print("Saved trained model to model/model.pkl") + +if __name__ == "__main__": + run_training() diff --git a/Labs/Airflow_Labs/Lab_1/dags/airflow_classification.py b/Labs/Airflow_Labs/Lab_1/dags/airflow_classification.py new file mode 100644 index 000000000..362175139 --- /dev/null +++ b/Labs/Airflow_Labs/Lab_1/dags/airflow_classification.py @@ -0,0 +1,85 @@ +# dags/airflow_classification.py +from datetime import datetime, timedelta +from airflow import DAG +from airflow.operators.python_operator import PythonOperator +from airflow import configuration as conf + +# Functions from your module (paths & logic live inside classification_lab.py) +from src.classification_lab import ( + load_data, # returns: pickled pandas DataFrame (bytes) + data_preprocessing, # takes: serialized df; returns: {"X_path": "...", "y_path": "..."} + build_save_model, # takes: dict with paths; returns: metrics dict + load_model_predict, # optional: sample_index kwarg; reads model/X from working_data +) + +from src.classification_lab import ( + load_data, data_preprocessing, build_save_model, load_model_predict, + predict_on_test_csv, # <-- add this +) + +# Allow pickled objects in XCom (since load_data returns a pickled DataFrame) +conf.set("core", "enable_xcom_pickling", "True") + +default_args = { + "owner": "your_name", + "start_date": datetime(2023, 9, 17), + "retries": 0, + "retry_delay": timedelta(minutes=5), +} + +dag = DAG( + dag_id="Airflow_Classification", + default_args=default_args, + description="Binary classification for credit risk (Logistic Regression)", + schedule_interval=None, # trigger manually from the UI + catchup=False, + tags=["ml", "classification"], +) + +# 1) LOAD CSV -> returns serialized dataframe via XCom +load_data_task = PythonOperator( + task_id="load_data_task", + python_callable=load_data, + dag=dag, +) + +# 2) PREPROCESS -> creates RISK_FLAG, scales features, writes X.pkl/y.pkl +# You can tweak the labeling rule here (risk_threshold default 0.25) +preprocess_task = PythonOperator( + task_id="data_preprocessing_task", + python_callable=data_preprocessing, + op_args=[load_data_task.output], # pass serialized df from previous task + op_kwargs={"risk_threshold": 0.25}, # change to 0.30 etc. if you want + dag=dag, +) + +# 3) TRAIN + SAVE MODEL -> returns metrics dict; also writes metrics.json and classification_model.pkl +train_task = PythonOperator( + task_id="train_and_save_model_task", + python_callable=build_save_model, + op_args=[preprocess_task.output], # {"X_path": "...", "y_path": "..."} + dag=dag, +) + +# 4) PREDICT (sample) -> loads saved model & X.pkl; logs a human-readable prediction +predict_task = PythonOperator( + task_id="sample_prediction_task", + python_callable=load_model_predict, + op_kwargs={"sample_index": 0}, # change index if you want to test a different row + dag=dag, +) +predict_on_test_task = PythonOperator( + task_id="predict_on_test_csv_task", + python_callable=predict_on_test_csv, + op_kwargs={"test_path": "/opt/airflow/dags/data/test.csv", "risk_threshold": 0.25}, + dag=dag, +) + +train_task >> [predict_task, predict_on_test_task] + + +# Define dependencies +load_data_task >> preprocess_task >> train_task >> predict_task + +if __name__ == "__main__": + dag.cli() diff --git a/Labs/Airflow_Labs/Lab_1/dags/src/classification_lab.py b/Labs/Airflow_Labs/Lab_1/dags/src/classification_lab.py new file mode 100755 index 000000000..8ea4d4a26 --- /dev/null +++ b/Labs/Airflow_Labs/Lab_1/dags/src/classification_lab.py @@ -0,0 +1,199 @@ +# Labs/Airflow_Labs/Lab_1/dags/src/classification_lab.py + +import os +import json +import pickle +import pandas as pd +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import StandardScaler +from sklearn.linear_model import LogisticRegression +from sklearn.metrics import classification_report, accuracy_score + +# -------- Paths INSIDE the Airflow containers -------- +DATA_PATH = "/opt/airflow/dags/data/file.csv" +TEST_DATA_PATH = "/opt/airflow/dags/data/test.csv" + +ARTIFACT_DIR = "/opt/airflow/working_data" +MODEL_PATH = os.path.join(ARTIFACT_DIR, "classification_model.pkl") +METRICS_PATH = os.path.join(ARTIFACT_DIR, "metrics.json") +X_PATH = os.path.join(ARTIFACT_DIR, "X.pkl") +Y_PATH = os.path.join(ARTIFACT_DIR, "y.pkl") + +# for test-time transforms & predictions +SCALER_PATH = os.path.join(ARTIFACT_DIR, "scaler.pkl") +FEATURES_PATH = os.path.join(ARTIFACT_DIR, "feature_columns.json") +NUMERIC_FEATURES_PATH = os.path.join(ARTIFACT_DIR, "numeric_feature_columns.json") +TEST_PREDICTIONS_PATH = os.path.join(ARTIFACT_DIR, "test_predictions.csv") + +os.makedirs(ARTIFACT_DIR, exist_ok=True) + + +# 1) LOAD +def load_data(): + df = pd.read_csv(DATA_PATH) + return pickle.dumps(df) # small enough for XCom when pickled + + +# 2) PREPROCESS +def data_preprocessing(serialized_df, risk_threshold: float = 0.25): + """ + Builds binary label RISK_FLAG = 1 if MINIMUM_PAYMENTS/BALANCE < threshold else 0, + scales numeric features, and persists: + - X.pkl / y.pkl + - scaler.pkl + - feature_columns.json (order!) + - numeric_feature_columns.json + """ + df = pickle.loads(serialized_df) + + # adjust these if your CSV headers differ + cid_col = "CUST_ID" + balance_col = "BALANCE" + minpay_col = "MINIMUM_PAYMENTS" + + for col in (balance_col, minpay_col): + if col not in df.columns: + raise ValueError(f"Required column '{col}' not found in dataset.") + + df[balance_col] = df[balance_col].fillna(0) + df[minpay_col] = df[minpay_col].fillna(0) + + eps = 1e-6 + df["RISK_FLAG"] = (df[minpay_col] / (df[balance_col] + eps) < risk_threshold).astype(int) + + if cid_col in df.columns: + df = df.drop(columns=[cid_col]) + + df = df.dropna(how="any").copy() + + X = df.drop(columns=["RISK_FLAG"]) + y = df["RISK_FLAG"] + + num_cols = X.select_dtypes(include="number").columns.tolist() + scaler = StandardScaler() + X_scaled = X.copy() + if num_cols: + X_scaled[num_cols] = scaler.fit_transform(X[num_cols]) + + # persist training artifacts + with open(X_PATH, "wb") as fx: + pickle.dump(X_scaled, fx) + with open(Y_PATH, "wb") as fy: + pickle.dump(y, fy) + with open(SCALER_PATH, "wb") as fs: + pickle.dump(scaler, fs) + with open(FEATURES_PATH, "w") as ff: + json.dump(list(X.columns), ff) + with open(NUMERIC_FEATURES_PATH, "w") as fn: + json.dump(num_cols, fn) + + return {"X_path": X_PATH, "y_path": Y_PATH} + + +# 3) TRAIN +def build_save_model(paths_dict, model_path: str = MODEL_PATH): + with open(paths_dict["X_path"], "rb") as fx: + X = pickle.load(fx) + with open(paths_dict["y_path"], "rb") as fy: + y = pickle.load(fy) + + X_train, X_test, y_train, y_test = train_test_split( + X, y, test_size=0.2, random_state=42, stratify=y + ) + + model = LogisticRegression(max_iter=1000) + model.fit(X_train, y_train) + + y_pred = model.predict(X_test) + acc = accuracy_score(y_test, y_pred) + report = classification_report(y_test, y_pred, output_dict=True) + + metrics = { + "Accuracy": acc, + "Precision_high_risk": report["1"]["precision"], + "Recall_high_risk": report["1"]["recall"], + "F1_high_risk": report["1"]["f1-score"], + "Support_high_risk": report["1"]["support"], + } + + with open(model_path, "wb") as f: + pickle.dump(model, f) + with open(METRICS_PATH, "w") as f: + json.dump(metrics, f, indent=2) + + return metrics + + +# 4) PREDICT (single sample from training split, just for a log demo) +def load_model_predict(model_path: str = MODEL_PATH, sample_index: int = 0): + with open(model_path, "rb") as f: + model = pickle.load(f) + with open(X_PATH, "rb") as fx: + X = pickle.load(fx) + + row = X.iloc[sample_index:sample_index + 1] if hasattr(X, "iloc") else X[sample_index:sample_index + 1] + pred = model.predict(row)[0] + label = "High Risk" if pred == 1 else "Low Risk" + return f"Sample index {sample_index} predicted class: {label}" + + +# 5) PREDICT ON TEST CSV (batch) + optional metrics if label columns exist +def predict_on_test_csv( + test_path: str = TEST_DATA_PATH, + risk_threshold: float = 0.25, + save_path: str = TEST_PREDICTIONS_PATH, +): + # load artifacts + with open(MODEL_PATH, "rb") as fm: + model = pickle.load(fm) + with open(SCALER_PATH, "rb") as fs: + scaler = pickle.load(fs) + with open(FEATURES_PATH, "r") as ff: + feature_cols = json.load(ff) + with open(NUMERIC_FEATURES_PATH, "r") as fn: + numeric_cols = set(json.load(fn)) + + # load test + df = pd.read_csv(test_path) + + # optional ground-truth for metrics + y_true = None + if {"BALANCE", "MINIMUM_PAYMENTS"}.issubset(df.columns): + eps = 1e-6 + y_true = ( + (df["MINIMUM_PAYMENTS"].fillna(0) / (df["BALANCE"].fillna(0) + eps) < risk_threshold) + .astype(int) + ) + + # build X with exact training columns + X_test = df.reindex(columns=feature_cols, fill_value=0).copy() + + # ensure numeric columns are numeric; scale with the saved scaler + for c in X_test.columns: + if c in numeric_cols: + X_test[c] = pd.to_numeric(X_test[c], errors="coerce").fillna(0) + if numeric_cols: + nc = [c for c in feature_cols if c in numeric_cols] + X_test[nc] = scaler.transform(X_test[nc]) + + # predict + y_pred = model.predict(X_test) + + # save predictions (keep CUST_ID if present) + out = pd.DataFrame({"prediction": y_pred}) + if "CUST_ID" in df.columns: + out.insert(0, "CUST_ID", df["CUST_ID"].values[: len(out)]) + out.to_csv(save_path, index=False) + + # return summary / metrics for logs + if y_true is not None and len(y_true) == len(y_pred): + acc = accuracy_score(y_true, y_pred) + rep = classification_report(y_true, y_pred, output_dict=True) + return { + "Saved": save_path, + "Test_Accuracy": acc, + "Test_Precision_high_risk": rep["1"]["precision"], + "Test_Recall_high_risk": rep["1"]["recall"], + "Test_F1_high_risk": rep["1"]["f1-score"], + } + return f"Saved {len(out)} predictions to {save_path}" diff --git a/Labs/Airflow_Labs/Lab_2/airflow_lab2/airflow_lab2_env/requirements.txt b/Labs/Airflow_Labs/Lab_2/airflow_lab2/airflow_lab2_env/requirements.txt new file mode 100644 index 000000000..71c67f060 --- /dev/null +++ b/Labs/Airflow_Labs/Lab_2/airflow_lab2/airflow_lab2_env/requirements.txt @@ -0,0 +1,8 @@ +apache-airflow +dvc[gs] +flask +scikit-learn +scipy +pandas +numpy + diff --git a/Labs/Data_Labs/DVC_Labs/dvc-gcs-lab b/Labs/Data_Labs/DVC_Labs/dvc-gcs-lab new file mode 160000 index 000000000..3e940d603 --- /dev/null +++ b/Labs/Data_Labs/DVC_Labs/dvc-gcs-lab @@ -0,0 +1 @@ +Subproject commit 3e940d603f338fd6c936671ec7dfe40a90175e8b diff --git a/Labs/Data_Labs/Data_Monitoring/Evidently_AI/Lab1.ipynb b/Labs/Data_Labs/Data_Monitoring/Evidently_AI/Lab1.ipynb index 1e1b0d13f..ae1472be8 100644 --- a/Labs/Data_Labs/Data_Monitoring/Evidently_AI/Lab1.ipynb +++ b/Labs/Data_Labs/Data_Monitoring/Evidently_AI/Lab1.ipynb @@ -2,12 +2,165 @@ "cells": [ { "cell_type": "code", - "execution_count": 12, + "execution_count": 1, "id": "e9841779", "metadata": {}, - "outputs": [], + 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"\u001b[2K Successfully uninstalled watchdog-2.1.6━━━\u001b[0m \u001b[32m 0/15\u001b[0m [watchdog]\n", + "\u001b[2K Attempting uninstall: cffi\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m 4/15\u001b[0m [dynaconf]\n", + "\u001b[2K Found existing installation: cffi 1.15.1━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m 4/15\u001b[0m [dynaconf]\n", + "\u001b[2K Uninstalling cffi-1.15.1:[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m 4/15\u001b[0m [dynaconf]\n", + "\u001b[2K Successfully uninstalled cffi-1.15.1m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m 6/15\u001b[0m [cffi]\n", + "\u001b[2K Attempting uninstall: certifi[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m 6/15\u001b[0m [cffi]\n", + "\u001b[2K Found existing installation: certifi 2024.2.2━━━━━━━━━━━━━\u001b[0m \u001b[32m 6/15\u001b[0m [cffi]\n", + "\u001b[2K Uninstalling certifi-2024.2.2:\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m 6/15\u001b[0m [cffi]\n", + "\u001b[2K Successfully 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cryptography-41.0.2━━━━━━━━━━━━\u001b[0m \u001b[32m 8/15\u001b[0m [requests]\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m15/15\u001b[0m [evidently]15\u001b[0m [evidently]hy]\n", + "\u001b[1A\u001b[2K\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "conda-repo-cli 1.0.41 requires requests_mock, which is not installed.\n", + "spyder 5.4.3 requires pyqt5<5.16, which is not installed.\n", + "spyder 5.4.3 requires pyqtwebengine<5.16, which is not installed.\n", + "streamlit 1.28.2 requires importlib-metadata<7,>=1.4, but you have importlib-metadata 8.7.0 which is incompatible.\n", + "streamlit 1.28.2 requires protobuf<5,>=3.20, but you have protobuf 6.32.1 which is incompatible.\n", + "tensorboard 2.15.1 requires protobuf<4.24,>=3.19.6, but you have protobuf 6.32.1 which is incompatible.\n", + "tensorflow-macos 2.15.0 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3, but you have protobuf 6.32.1 which is incompatible.\n", + "conda-repo-cli 1.0.41 requires clyent==1.2.1, but you have clyent 1.2.2 which is incompatible.\n", + "conda-repo-cli 1.0.41 requires nbformat==5.4.0, but you have nbformat 5.7.0 which is incompatible.\n", + "conda-repo-cli 1.0.41 requires requests==2.28.1, but you have requests 2.32.5 which is incompatible.\n", + "pyopenssl 23.2.0 requires cryptography!=40.0.0,!=40.0.1,<42,>=38.0.0, but you have cryptography 46.0.3 which is incompatible.\n", + "anaconda-cloud-auth 0.1.4 requires pydantic<2.0, but you have pydantic 2.11.9 which is incompatible.\n", + "langchain-google-genai 0.0.5 requires google-generativeai<0.4.0,>=0.3.1, but you have google-generativeai 0.3.0 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0mSuccessfully installed certifi-2025.11.12 cffi-2.0.0 cryptography-46.0.3 deprecation-2.1.0 dynaconf-3.2.12 evidently-0.7.0 faker-38.0.0 iterative-telemetry-0.0.10 litestar-2.18.0 litestar-htmx-0.5.0 multipart-1.3.0 polyfactory-3.0.0 requests-2.32.5 rich-click-1.9.4 watchdog-6.0.0\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.3\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n" + ] + } + ], "source": [ - "# !pip install evidently==0.7.0" + "!pip install evidently==0.7.0" ] }, { @@ -17,10 +170,11 @@ "metadata": {}, "outputs": [], "source": [ + "# 2. Imports\n", "import pandas as pd\n", "import numpy as np\n", "from sklearn import datasets\n", - " \n", + "\n", "from evidently import Dataset\n", "from evidently import DataDefinition\n", "from evidently import Report\n", @@ -39,20 +193,29 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "aad961ce", "metadata": {}, "outputs": [], "source": [ - "ws = CloudWorkspace(token=\"dG9rbgFi8CrFvQBKPIhN6d/kpRvO8vkG8T/AGnYJy1PjnHXRPgBQMNvIZJd8owXHoMo8MlLol5E/SPIgGqP265+d1/WYD8Xte+ClVtp9DxoCoj3VL6EzxF66+9xZx8EqJo/s/lVnUC+GeKakpg/NNew6wmDgbeUu23bK\", url=\"https://app.evidently.cloud\")" + "ws = CloudWorkspace(token=\"dG9rbgFY3evYs+9PhqYzP3rpF7raoKVF715yihr1E4zGVMEqQQBQjFqGuUMqpQt+/GUBdMif5NxCoh02PMZKVMJJIaU1znsHw+NTn8khiuw83gdwbnQH32nELg+vOuf1Pk9woINHF+fkmnwQMSTi8rWKztQHBk1DjF0j\", url=\"https://app.evidently.cloud\")" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "6c0aa509", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/shiviesaksenaa/anaconda3/lib/python3.11/site-packages/sklearn/datasets/_openml.py:1022: FutureWarning: The default value of `parser` will change from `'liac-arff'` to `'auto'` in 1.4. You can set `parser='auto'` to silence this warning. Therefore, an `ImportError` will be raised from 1.4 if the dataset is dense and pandas is not installed. Note that the pandas parser may return different data types. See the Notes Section in fetch_openml's API doc for details.\n", + " warn(\n" + ] + } + ], "source": [ "adult_data = datasets.fetch_openml(name='adult', version=2, as_frame='auto')\n", "adult = adult_data.frame" @@ -60,7 +223,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "f1f8a510", "metadata": {}, "outputs": [], @@ -71,7 +234,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "d27ad72a", "metadata": {}, "outputs": [], @@ -84,12 +247,12 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 14, "id": "b10d2e5f", "metadata": {}, "outputs": [], "source": [ - "eval_data_1 = Dataset.from_pandas(\n", + "eval_data_prod= Dataset.from_pandas(\n", " pd.DataFrame(adult_prod),\n", " data_definition=schema\n", ")" @@ -97,12 +260,12 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 15, "id": "4af315db", "metadata": {}, "outputs": [], "source": [ - "eval_data_2 = Dataset.from_pandas(\n", + "eval_data_ref= Dataset.from_pandas(\n", " pd.DataFrame(adult_ref),\n", " data_definition=schema\n", ")" @@ -110,23 +273,44 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 25, "id": "95891600", "metadata": {}, "outputs": [], "source": [ + "from evidently.presets import DataDriftPreset, DataSummaryPreset\n", + "\n", "report = Report([\n", - " DataDriftPreset() \n", + " DataSummaryPreset(), # data quality + stats\n", + " DataDriftPreset(), # drift detection\n", "])\n", - "\n", - "my_eval = report.run(eval_data_1, eval_data_2)" + "my_report = report.run(eval_data_prod, eval_data_ref)" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 26, "id": "70811c80", "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved as adult_data_monitoring_report.html\n" + ] + } + ], + "source": [ + "my_report.save_html(\"adult_data_monitoring_report.html\")\n", + "print(\"Saved as adult_data_monitoring_report.html\")" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "7082e38a", + "metadata": {}, "outputs": [ { "data": { @@ -193,12 +377,12 @@ " </style>\n", " \n", " <script>\n", - " var metric_27249d60d220429a9377c77d17ca4f06 = {"name": "Report", "widgets": [{"type": "group", "title": "", "size": 2, "id": "0199bc22-e111-7897-93dc-733ed64c2d70", "details": "", "alertsPosition": null, "alertStats": null, "params": null, "insights": [], "alerts": [], "tabs": [], "widgets": [{"type": "counter", "title": "", "size": 2, "id": "0199bc22-dc92-76fb-8358-c452c301039a", "details": "", "alertsPosition": null, "alertStats": null, "params": {"counters": [{"value": "Dataset Drift", "label": "Dataset Drift is NOT detected. Dataset drift detection threshold is 0.5"}]}, "insights": [], "additionalGraphs": [], "alerts": [], "tabs": [], "widgets": [], "pageSize": 5, "source_fingerprint": null, "linked_metrics": null}, {"type": "counter", "title": "", "size": 2, "id": "0199bc22-dc93-7501-8c2d-2a0156656ca6", "details": "", "alertsPosition": null, "alertStats": null, "params": {"counters": [{"value": "15", "label": "Columns"}, {"value": "5", "label": "Drifted Columns"}, {"value": "0.333", "label": "Share of Drifted Columns"}]}, "insights": [], "additionalGraphs": [], "alerts": [], "tabs": [], "widgets": [], "pageSize": 5, "source_fingerprint": null, "linked_metrics": null}, {"type": "counter", "title": "", "size": 2, "id": "0199bc22-e108-7866-b31d-80807993c326", "details": "", "alertsPosition": null, "alertStats": null, "params": {"counters": [{"value": "", "label": "Data Drift Summary"}]}, "insights": [], "additionalGraphs": [], "alerts": [], "tabs": [], "widgets": [], "pageSize": 5, "source_fingerprint": null, "linked_metrics": null}, {"type": "big_table", "title": "Drift is detected for 33.333% of columns (5 out of 15).", "size": 2, "id": "0199bc22-e109-73b9-b8c1-bb47d3188a84", "details": "", "alertsPosition": "row", "alertStats": null, "params": {"rowsPerPage": 10, "columns": [{"title": "Column", "field": "column_name"}, {"title": "Type", "field": "column_type"}, {"title": "Reference Distribution", "field": "reference_distribution", "type": "histogram", "options": {"xField": "x", "yField": "y", "color": "#ed0400"}}, {"title": "Current Distribution", "field": "current_distribution", "type": "histogram", "options": {"xField": "x", "yField": "y", "color": "#ed0400"}}, {"title": "Data Drift", "field": "data_drift"}, {"title": "Stat Test", "field": "stattest_name"}, {"title": "Drift Score", "field": "drift_score"}], "data": [{"details": {"parts": [{"title": "DATA DISTRIBUTION", "id": "0199bc22-dfaf-7154-af59-bb0eb6b68a14", "type": "widget"}]}, "column_name": "education", "column_type": "cat", "stattest_name": "Jensen-Shannon distance", "reference_distribution": {"x": ["10th", "11th", "12th", "1st-4th", "5th-6th", "7th-8th", "9th", "Assoc-acdm", "Assoc-voc", "Bachelors", "Doctorate", "HS-grad", "Masters", "Preschool", "Prof-school", "Some-college"], "y": [1389, 1812, 657, 247, 509, 955, 756, 1601, 2061, 0, 594, 0, 2657, 83, 834, 0]}, "current_distribution": {"x": ["10th", "11th", "12th", "1st-4th", "5th-6th", "7th-8th", "9th", "Assoc-acdm", "Assoc-voc", "Bachelors", "Doctorate", "HS-grad", "Masters", "Preschool", "Prof-school", "Some-college"], "y": [0, 0, 0, 0, 0, 0, 0, 0, 0, 8025, 0, 15784, 0, 0, 0, 10878]}, "data_drift": "Detected", "drift_score": 0.832555}, {"details": {"parts": [{"title": "DATA DRIFT", "id": "0199bc22-dfc2-79b6-934a-dcf6cb1a7941", "type": "widget"}, {"title": "DATA DISTRIBUTION", "id": "0199bc22-dfd4-774c-b9f6-fa5f91bb98fb", "type": "widget"}]}, "column_name": "education-num", "column_type": "num", "stattest_name": "Wasserstein distance (normed)", "reference_distribution": {"x": [1.0, 2.5, 4.0, 5.5, 7.0, 8.5, 10.0, 11.5, 13.0, 14.5, 16.0], "y": [0.015542211232779936, 0.023972683386318142, 0.08058401036147415, 0.06541858000706464, 0.11628399858707171, 0.0, 0.09706817379018015, 0.07540327328388084, 0.12513834922877665, 0.06725538678912045]}, "current_distribution": {"x": [9.0, 9.4, 9.8, 10.2, 10.6, 11.0, 11.4, 11.8, 12.2, 12.6, 13.0], "y": [1.1376019834520126, 0.0, 0.7840113010638019, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.5783867154841866]}, "data_drift": "Detected", "drift_score": 0.617697}, {"details": {"parts": [{"title": "DATA DRIFT", "id": "0199bc22-dfe5-7936-8f7e-af32daf15c3f", "type": "widget"}, {"title": "DATA DISTRIBUTION", "id": "0199bc22-dff5-7e4f-98ff-0f597150146e", "type": "widget"}]}, "column_name": "age", "column_type": "num", "stattest_name": "Wasserstein distance (normed)", "reference_distribution": {"x": [17.0, 24.3, 31.6, 38.9, 46.2, 53.5, 60.8, 68.1, 75.4, 82.7, 90.0], "y": [0.02104876054252575, 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"display_name": "base", "language": "python", "name": "python3" }, @@ -853,7 +1037,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.11.4" } }, "nbformat": 4, diff --git a/Labs/Data_Labs/Data_Monitoring/Evidently_AI/README.md b/Labs/Data_Labs/Data_Monitoring/Evidently_AI/README.md new file mode 100644 index 000000000..3f0a681d4 --- /dev/null +++ b/Labs/Data_Labs/Data_Monitoring/Evidently_AI/README.md @@ -0,0 +1,61 @@ +Data Monitoring with Evidently AI – README + +Overview + +In this lab, I used Evidently AI to perform data monitoring and evaluate how the distribution of incoming (production) data compares to a reference dataset. The goal was to detect data drift, analyze data quality, and generate a shareable HTML monitoring report that can be archived or sent to collaborators. + +This exercise helped me understand how real-world MLOps systems track changes in data over time to maintain model reliability. + + What I Did in This Lab + +1. Loaded the Adult Income dataset +2. Split it into: + +2.1 Reference data (baseline/training-like) + +2.2 Production data (simulated live data) + +3. Defined a schema for Evidently: + +3.1 Numerical columns + +3.2 Categorical columns + +4. Created an Evidently monitoring report using: + +4.1 DataSummaryPreset → data quality + statistics + +4.2 DataDriftPreset → feature-level and dataset-level drift detection + +5. Ran the report comparing reference and production data +6. Saved the full report as an HTML file for explainability and versioning + +Why This Matters: + +Machine learning systems depend on the assumption that new data looks like training data. +But in production: feature distributions drift, new categories appear +, missing values increase and schemas change unexpectedly. + +These changes reduce model performance. + +By using Evidently, I learned how to automatically detect these issues and generate reports that track data health over time. This is a core concept in MLOps, especially when models are deployed long-term. + + +What I Changed From the Original Lab + +To extend the base lab and demonstrate my understanding, I made the following enhancements: + +A) I added the DataSummaryPreset. + +This allowed me to monitor: missing values, schema consistency, cardinality, and summary statistics + +This gives a more complete picture of data health instead of drift alone. + +Result : I saved the report as an HTML file. + +Exporting the report to: adult_data_monitoring_report.html + +This allowed me to: share the results, archive the report and keep historical monitoring logs + +This is a standard MLOps best practice for explainability and version control. + diff --git a/Labs/Data_Labs/Data_Monitoring/Evidently_AI/adult_data_monitoring_report.html b/Labs/Data_Labs/Data_Monitoring/Evidently_AI/adult_data_monitoring_report.html new file mode 100644 index 000000000..0c4df0cfb --- /dev/null +++ b/Labs/Data_Labs/Data_Monitoring/Evidently_AI/adult_data_monitoring_report.html @@ -0,0 +1,683 @@ + + + + +