profitboost / transformers.py
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from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.preprocessing import OneHotEncoder
from sklearn.cluster import KMeans
import pandas as pd
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