From cdc769fd334cd6e143183b55f225785489be4ee8 Mon Sep 17 00:00:00 2001 From: coolneng Date: Tue, 12 Jan 2021 23:00:19 +0100 Subject: [PATCH] Add summary --- default.nix | 5 --- docs/Summary.org | 98 ++++++++++++++++++++++++++++++++++++++++++++- docs/Summary.pdf | Bin 0 -> 526860 bytes docs/assets/F1.png | Bin 0 -> 10382 bytes docs/assets/F2.png | Bin 0 -> 10242 bytes 5 files changed, 96 insertions(+), 7 deletions(-) delete mode 100644 default.nix create mode 100644 docs/Summary.pdf create mode 100644 docs/assets/F1.png create mode 100644 docs/assets/F2.png diff --git a/default.nix b/default.nix deleted file mode 100644 index d13b774..0000000 --- a/default.nix +++ /dev/null @@ -1,5 +0,0 @@ -{ pkgs ? import { } }: - -with pkgs; - -poetry2nix.mkPoetryApplication { projectDir = ./.; } diff --git a/docs/Summary.org b/docs/Summary.org index 09bd8a6..dae18e6 100644 --- a/docs/Summary.org +++ b/docs/Summary.org @@ -1,7 +1,7 @@ #+TITLE: Práctica 3 #+SUBTITLE: Inteligencia de Negocio #+AUTHOR: Amin Kasrou Aouam -#+DATE: 2021-01-01 +#+DATE: 2021-01-12 #+PANDOC_OPTIONS: template:~/.pandoc/templates/eisvogel.latex #+PANDOC_OPTIONS: listings:t #+PANDOC_OPTIONS: toc:t @@ -18,9 +18,103 @@ En esta práctica, resolveremos un problema de clasificación multiclase, en con ** Preprocesamiento de datos *** Valores nulos + +Nuestro /dataset/ contiene bastantes valores nulos, optamos por estrategias diferentes según las columnas: + +- Eliminación: tipo marchas, descuento, ciudad +- Imputación: asientos, motor cc, potencia + +El criterio que seleccionamos es el número de instancias nulas, en el caso de que sean muchas optamos por imputar, para mantener un número adecuado de datos. + +La implementación se encuentra en la siguiente función: + +#+begin_src python +def process_null_values(df_list): + drop_columns = ["tipo_marchas", "descuento", "ciudad"] + fill_columns = ["asientos", "motor_cc", "potencia"] + for df in df_list: + for column in fill_columns: + if column == "asientos": + df[column].fillna(value=df[column].median(), inplace=True) + else: + df[column].fillna( + value=df[column].str.extract("(\d+)").mean(), inplace=True + ) + df.drop(columns=drop_columns, inplace=True) + df.dropna(inplace=True) + return df_list +#+end_src + *** Valores no numéricos + +Ciertas columnas contienen valores alfanúmericos, aunque se nos proporcionan distintos archivos CSV para realizar un /mapping/. En este caso, utilizamos un *LabelEncoder*, y como entrada le damos el CSV correspondiente. + +Es primordial usar el mismo /LabelEncoder/ para los datos de entrenamiento como de test. La implementación se encuentra en la siguiente función: + +#+begin_src python +def encode_columns(df_list): + label_encoder = LabelEncoder() + files = [ + "ao", + "asientos", + "combustible", + "consumo", + "kilometros", + "mano", + "motor_cc", + "nombre", + "potencia", + ] + for data in files: + for df in df_list: + label = label_encoder.fit(read_csv("data/" + data + ".csv", squeeze=True)) + if data == "ao": + df["año"] = label.transform(df["año"]) + else: + df[data] = label.transform(df[data]) + return df_list +#+end_src + *** Balanceo de clases + +Observamos que la mayoría de coches son de la categoría de precio 3, lo cual no es idóneo para entrenar un modelo de inteligencia artificial. + +Debemos realizar un balanceo de las clases, en este caso optamos por usar el modelo *SMOTEEEN*, que combina un /over-sampling/ mediante *SMOTE* y una limpieza gracias a /Edited Nearest Neighbours (ENN)/. + +La implementación se encuentra en esta función: + +#+begin_src python +def balance_training_data(df): + smote_enn = SMOTEENN(random_state=42) + data, target = split_data_target(df=df, dataset="data") + balanced_data, balanced_target = smote_enn.fit_resample(data, target) + balanced_data_df = DataFrame( + balanced_data, columns=df.columns.difference(["precio_cat"]) + ) + balanced_target_df = DataFrame(balanced_target, columns=["precio_cat"]) + return balanced_data_df, balanced_target_df +#+end_src + ** Elección de algoritmo -** Configuración del algoritmo + +Elegimos el algoritmo *GradientBoostingClassifier*, que pertenece a los algoritmos de /ensemble/. Éstos combinan las predicciones de varios clasificadores, con el objetivo de mejorar la generalización y la robustez de las predicciones. + +En particular, pertenece a la familia de /boosting methods/, cuya característica es que los clasificadores se crean de forma secuencial, y uno de ellos trata de reducir el sesgo de los demás. + ** Resultados obtenidos + +Al ejecutar el programa en local obtenemos los siguientes resultados: + +#+CAPTION: Resultados de ejecución +[[./assets/F1.png]] + +Desafortunadamente, en la plataforma Kaggle obtenemos unos resultados pésimos: + +#+CAPTION: Resultados de Kaggle +[[./assets/F2.png]] + ** Análisis de resultados + +Debido a la discrepancia entre los resultados de la ejecución en local, y de la plataforma Kaggle, intuimos que debe de haber un problema en el preprocesamiento de datos. + +También es posible que el modelo no sea óptimo para la tarea, aunque no justificaría un rendimiento tan bajo, puede contribuir a ello. diff --git a/docs/Summary.pdf 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