docs/Summary.org

@@ -1,26 +0,0 @@
-#+TITLE: Práctica 3
-#+SUBTITLE: Inteligencia de Negocio
-#+AUTHOR: Amin Kasrou Aouam
-#+DATE: 2021-01-01
-#+PANDOC_OPTIONS: template:~/.pandoc/templates/eisvogel.latex
-#+PANDOC_OPTIONS: listings:t
-#+PANDOC_OPTIONS: toc:t
-#+PANDOC_METADATA: lang=es
-#+PANDOC_METADATA: titlepage:t
-#+PANDOC_METADATA: listings-no-page-break:t
-#+PANDOC_METADATA: toc-own-page:t
-#+PANDOC_METADATA: table-use-row-colors:t
-#+PANDOC_METADATA: logo:/home/coolneng/Photos/Logos/UGR.png
-* Práctica 3
-** Introducción
-
-En esta práctica, resolveremos un problema de clasificación multiclase, en concreto, trataremos de predecir la categoría de precio de una serie de coches
-
-** Preprocesamiento de datos
-*** Valores nulos
-*** Valores no numéricos
-*** Balanceo de clases
-** Elección de algoritmo
-** Configuración del algoritmo
-** Resultados obtenidos
-** Análisis de resultados

src/preprocessing.py

@@ -1,7 +1,6 @@
 from pandas import DataFrame, read_csv
 from sklearn.preprocessing import LabelEncoder
 from sklearn.model_selection import KFold
-from imblearn.combine import SMOTETomek
 
 
 def construct_dataframes(train, test):
@@ -13,17 +12,16 @@ def construct_dataframes(train, test):
     return df_list
 
 
-def rename_columns(df_list) -> DataFrame:
-    for df in df_list:
-        df.columns = df.columns.str.lower()
-    return df_list
-
-
 def drop_null_values(df_list):
     for df in df_list:
         df.dropna(inplace=True)
-        df.drop(columns="tipo_marchas", inplace=True)
+        df.drop(columns="Tipo_marchas", inplace=True)
-        df["descuento"].fillna(0)
+    return df_list
+
+
+def rename_columns(df_list) -> DataFrame:
+    for df in df_list:
+        df.columns = df.columns.str.lower()
     return df_list
 
 
@@ -57,26 +55,6 @@ def encode_columns(df_list):
     return df_list
 
 
-def split_data_target(df, dataset):
-    if dataset == "data":
-        df.drop(columns="id", inplace=True)
-        data = df.drop(columns=["precio_cat"])
-        target = df["precio_cat"]
-    else:
-        data = df.drop(columns=["id"])
-        target = df["id"]
-    return data, target
-
-
-def balance_training_data(df):
-    smote_tomek = SMOTETomek(random_state=42)
-    data, target = split_data_target(df=df, dataset="data")
-    balanced_data, balanced_target = smote_tomek.fit_resample(data, target)
-    balanced_data_df = DataFrame(balanced_data, columns=data.columns)
-    balanced_target_df = DataFrame(balanced_target, columns=target.columns)
-    return balanced_data_df, balanced_target_df
-
-
 def split_k_sets(df):
     k_fold = KFold(shuffle=True, random_state=42)
     return k_fold.split(df)
@@ -88,6 +66,4 @@ def parse_data(train, test):
     processed_df_list = drop_null_values(renamed_df_list)
     numeric_df_list = trim_column_names(processed_df_list)
     encoded_df_list = encode_columns(numeric_df_list)
-    train_data, train_target = balance_training_data(encoded_df_list[0])
+    return encoded_df_list
-    test_data, test_ids = split_data_target(encoded_df_list[1], dataset="test")
-    return train_data, train_target, test_data, test_ids

src/processing.py

@@ -1,51 +0,0 @@
-from numpy import mean
-from pandas import DataFrame
-from sklearn.metrics import accuracy_score
-from sklearn.model_selection import cross_val_score
-from sklearn.ensemble import GradientBoostingClassifier
-
-from preprocessing import parse_data, split_k_sets
-
-
-def predict_data(train_data, train_target, test_data, test_ids):
-    model = GradientBoostingClassifier(random_state=42)
-    accuracy_scores = []
-    for train_index, test_index in split_k_sets(train_data):
-        model.fit(train_data.iloc[train_index], train_target.iloc[train_index])
-        prediction = model.predict(train_data.iloc[test_index])
-        accuracy_scores.append(
-            accuracy_score(train_target.iloc[test_index], prediction)
-        )
-    cv_score = cross_val_score(model, train_data, train_target)
-    evaluate_performance(
-        accuracy=mean(accuracy_scores),
-        cv_score=mean(cv_score),
-    )
-    predictions = model.predict(test_data)
-    export_results(ids=test_ids, prediction=predictions)
-
-
-def evaluate_performance(accuracy, cv_score):
-    print("Accuracy Score: " + str(accuracy))
-    print("Cross validation score: " + str(cv_score))
-
-
-def export_results(ids, prediction):
-    result_df = DataFrame({"id": ids, "Precio_cat": prediction})
-    result_df.to_csv(path_or_buf="data/results.csv", index=False)
-
-
-def main():
-    train_data, train_target, test_data, test_ids = parse_data(
-        train="data/train.csv", test="data/test.csv"
-    )
-    predict_data(
-        train_data=train_data,
-        train_target=train_target,
-        test_data=test_data,
-        test_ids=test_ids,
-    )
-
-
-if __name__ == "__main__":
-    main()