Update prediction.py

prediction.py

@@ -2,7 +2,61 @@
 
 import joblib
 import pandas as pd
-from transformers import UnitPriceTransformer
+from sklearn.base import BaseEstimator, TransformerMixin
+from sklearn.preprocessing import OneHotEncoder
+from sklearn.cluster import KMeans
+import pandas as pd
+import joblib
+
+class UnitPriceTransformer(BaseEstimator, TransformerMixin):
+    def fit(self, X, y=None):
+        return self
+    
+    def transform(self, X):
+        X['unit_price'] = X['sales'] / X['quantity']
+        return X
+
+class KMeansAndLabelTransformer(BaseEstimator, TransformerMixin):
+    def __init__(self, n_clusters=3):
+        self.n_clusters = n_clusters
+        self.kmeans = KMeans(n_clusters=n_clusters, random_state=42)
+    
+    def fit(self, X, y=None):
+        # Fit the KMeans model on the 'unit_price', ensuring it's reshaped for a single feature
+        self.kmeans.fit(X[['unit_price']])
+        return self
+    
+    def transform(self, X):
+        # Predict the cluster labels
+        cluster_labels = self.kmeans.predict(X[['unit_price']])
+        
+        # Convert cluster labels to strings for concatenation
+        # Create a new DataFrame column for 'distinct_cluster_label'
+        # Here, we use the apply function with a lambda to concatenate the string representations safely
+        X = X.copy()  # Avoid SettingWithCopyWarning
+        X['cluster_labels_str'] = cluster_labels.astype(str)
+        X['distinct_cluster_label'] = X.apply(lambda row: row['cluster_labels_str'] + "_" + str(row['sub_category']), axis=1)
+        
+        # Now that 'distinct_cluster_label' is created, 'cluster_labels_str' can be dropped
+        X.drop(['cluster_labels_str'], axis=1, inplace=True)
+        
+        return X
+
+
+
+class DynamicOneHotEncoder(BaseEstimator, TransformerMixin):
+    def fit(self, X, y=None):
+        self.encoder = OneHotEncoder(handle_unknown='ignore')
+        self.encoder.fit(X[['distinct_cluster_label']])
+        return self
+    
+    def transform(self, X):
+        encoded_features = self.encoder.transform(X[['distinct_cluster_label']]).toarray()
+        encoded_df = pd.DataFrame(encoded_features, columns=self.encoder.get_feature_names_out(['distinct_cluster_label']))
+        X.reset_index(drop=True, inplace=True)
+        result = pd.concat([X, encoded_df], axis=1)
+        result.drop(['distinct_cluster_label', 'sub_category', 'unit_price'], axis=1, inplace=True)  # Drop original columns if not needed
+        return result
 
 # Load the preprocessing pipeline
 pipeline = joblib.load('full_pipeline_with_unit_price.pkl')