Added mlops

debug.py

@@ -1 +1,5 @@
-import herrewebpy
+from herrewebpy.mlops import anomaly_scoring
+import seaborn as sns
+
+df = sns.load_dataset('iris')
+anomaly_scoring.train_model(df)

herrewebpy/mlops/__init__.py

@@ -0,0 +1 @@
+from . import *

herrewebpy/mlops/anomaly_scoring.py

@@ -0,0 +1,35 @@
+from herrewebpy import logger
+
+import pandas as pd
+import numpy as np
+import tensorflow as tf
+from sklearn.preprocessing import StandardScaler
+
+
+def perceptron_build_model(df, hidden_units=64):
+    numerical_features = df.select_dtypes(include=[np.number])
+
+    # Standardize the numerical features
+    scaler = StandardScaler()
+    scaled_data = scaler.fit_transform(numerical_features)
+
+    # Define the Perceptron model
+    input_dim = scaled_data.shape[1]
+
+    model = tf.keras.Sequential([
+        tf.keras.layers.Input(shape=(input_dim,)),
+        tf.keras.layers.Dense(hidden_units, activation='relu'),
+        tf.keras.layers.Dense(1)  # Output layer for regression
+    ])
+
+    # Compile the model
+    model.compile(optimizer='adam', loss='mean_squared_error')
+    return model, scaled_data
+
+
+def train_model(df):
+    model, scaled_data = perceptron_build_model(df)
+    epochs = 100
+    batch_size = 32
+
+    model.fit(scaled_data, scaled_data, epochs=epochs, batch_size=batch_size, verbose=1)

herrewebpy/trains/ns_api.py

@@ -1,4 +1,5 @@
 import requests
 from herrewebpy import logger
 
-logger.info(f'Running here')
+logger.info(f'Running {__file__}')
+

requirements.txt

@@ -1,2 +1,6 @@
 pandas
-numpy
+numpy
+tensorflow
+sklearn
+seaborn
+scikit-learn