From bb67366284791d27cb0ad6ddc3c15373a843769f Mon Sep 17 00:00:00 2001 From: coolneng Date: Tue, 10 Nov 2020 22:28:40 +0100 Subject: [PATCH] Translate summary to spanish --- docs/Experiments.org | 71 ------------------- docs/Summary.org | 158 +++++++++++++++++++++++++++++++++++++++++++ docs/Summary.pdf | Bin 0 -> 466039 bytes 3 files changed, 158 insertions(+), 71 deletions(-) delete mode 100644 docs/Experiments.org create mode 100644 docs/Summary.org create mode 100644 docs/Summary.pdf diff --git a/docs/Experiments.org b/docs/Experiments.org deleted file mode 100644 index 1a32207..0000000 --- a/docs/Experiments.org +++ /dev/null @@ -1,71 +0,0 @@ -* Experiments - -We will first try to gather information about our dataset, by evaluating the statistics of our attributes. - -#+BEGIN_SRC python -from pandas import read_csv -from sklearn.preprocessing import LabelEncoder - - -def replace_values(df): - columns = ["BI-RADS", "Margin", "Density", "Age"] - for column in columns: - df[column].fillna(value=df[column].mean(), inplace=True) - return df - - -def process_na(df, action): - if action == "drop": - return df.dropna() - return replace_values(df) - - -def encode_columns(df): - encoder = LabelEncoder() - encoder.fit(df["Shape"]) - - -def parse_data(source, action): - df = read_csv(filepath_or_buffer=source, na_values="?") - processed_df = process_na(df, action) - return processed_df -#+END_SRC - -#+RESULTS: - - -#+BEGIN_SRC python -df = parse_data("../data/mamografia.csv", "drop") -print(df.describe()) -#+END_SRC - -#+RESULTS: -: BI-RADS Age Margin Density -: count 847.000000 847.000000 847.000000 847.000000 -: mean 4.322314 55.842975 2.833530 2.909091 -: std 0.703762 14.603754 1.564049 0.370292 -: min 0.000000 18.000000 1.000000 1.000000 -: 25% 4.000000 46.000000 1.000000 3.000000 -: 50% 4.000000 57.000000 3.000000 3.000000 -: 75% 5.000000 66.000000 4.000000 3.000000 -: max 6.000000 96.000000 5.000000 4.000000 - -We observe that *margin* and *density* are the columns with the most unknown values. The age group of our cohort is middle aged, the BI-RADS score is mostly in the suspicious category, the density is mostly low and the margin belongs to the microlobulated/obscured category. - -We'll try to impute values, instead of dropping them, when they're invalid. - -#+BEGIN_SRC python -df = parse_data("../data/mamografia.csv", "replace") -print(df.describe()) -#+END_SRC - -#+RESULTS: -: BI-RADS Age Margin Density -: count 961.000000 961.000000 961.000000 961.000000 -: mean 4.296142 55.487448 2.796276 2.910734 -: std 0.705555 14.442373 1.526880 0.365074 -: min 0.000000 18.000000 1.000000 1.000000 -: 25% 4.000000 45.000000 1.000000 3.000000 -: 50% 4.000000 57.000000 3.000000 3.000000 -: 75% 5.000000 66.000000 4.000000 3.000000 -: max 6.000000 96.000000 5.000000 4.000000 diff --git a/docs/Summary.org b/docs/Summary.org new file mode 100644 index 0000000..df12650 --- /dev/null +++ b/docs/Summary.org @@ -0,0 +1,158 @@ +#+TITLE: Práctica 1 +#+SUBTITLE: Inteligencia de Negocio +#+AUTHOR: Amin Kasrou Aouam +#+DATE: 2020-11-10 +#+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 1 + +** Introducción + +En esta práctica, usaremos distintos algoritmos de aprendizaje automático para resolver un problema de clasificación. + +** Procesado de datos + +Antes de proceder con el entrenamiento de los distintos modelos, debemos realizar un preprocesado de los datos, para asegurarnos que nuestros modelos aprenden de un /dataset/ congruente. + +La integridad de la lógica del preprocesado se encuentra en el archivo /preprocessing.py/, cuyo contenido mostramos aquí: + +#+begin_src python +from pandas import read_csv +from sklearn.preprocessing import LabelEncoder +from sklearn.model_selection import KFold + + +def replace_values(df): + columns = ["BI-RADS", "Margin", "Density", "Age"] + for column in columns: + df[column].fillna(value=df[column].mean(), inplace=True) + return df + + +def process_na(df, action): + if action == "drop": + return df.dropna() + elif action == "fill": + return replace_values(df) + else: + print("Unknown action selected. The choices are: ") + print("fill: fills the na values with the mean") + print("drop: drops the na values") + exit() + + +def encode_columns(df): + label_encoder = LabelEncoder() + encoded_df = df.copy() + encoded_df["Shape"] = label_encoder.fit_transform(df["Shape"]) + encoded_df["Severity"] = label_encoder.fit_transform(df["Severity"]) + return encoded_df + + +def split_train_target(df): + train_data = df.drop(columns=["Severity"]) + target_data = df["Severity"] + return train_data, target_data + + +def split_k_sets(df): + k_fold = KFold(shuffle=True, random_state=42) + return k_fold.split(df) + + +def parse_data(source, action): + df = read_csv(filepath_or_buffer=source, na_values="?") + processed_df = process_na(df=df, action=action) + encoded_df = encode_columns(df=processed_df) + test_data, target_data = split_train_target(df=encoded_df) + return test_data, target_data +#+end_src + +#+RESULTS: + +A continuación, mostraremos cada uno de los pasos que realizamos para obtener el /dataset/ final: + +*** Valores nulos: + +Nuestro /dataset/ contiene valores nulos, representados mediante un signo de interrogación (?). Optamos por evaluar 2 estrategias: + +1. Eliminar los valores nulos + +#+BEGIN_SRC python +df = read_csv(filepath_or_buffer="../data/mamografia.csv", na_values="?") +processed_df = process_na(df=df, action="drop") +print("DataFrame sin preprocesamiento: ") +print(df.describe()) +print("DataFrame sin preprocesamiento: ") +print(processed_df.describe()) +#+END_SRC + +#+RESULTS: +#+begin_example +DataFrame sin preprocesamiento: + BI-RADS Age Margin Density +count 959.000000 956.000000 913.000000 885.000000 +mean 4.296142 55.487448 2.796276 2.910734 +std 0.706291 14.480131 1.566546 0.380444 +min 0.000000 18.000000 1.000000 1.000000 +25% 4.000000 45.000000 1.000000 3.000000 +50% 4.000000 57.000000 3.000000 3.000000 +75% 5.000000 66.000000 4.000000 3.000000 +max 6.000000 96.000000 5.000000 4.000000 +DataFrame sin preprocesamiento: + BI-RADS Age Margin Density +count 847.000000 847.000000 847.000000 847.000000 +mean 4.322314 55.842975 2.833530 2.909091 +std 0.703762 14.603754 1.564049 0.370292 +min 0.000000 18.000000 1.000000 1.000000 +25% 4.000000 46.000000 1.000000 3.000000 +50% 4.000000 57.000000 3.000000 3.000000 +75% 5.000000 66.000000 4.000000 3.000000 +max 6.000000 96.000000 5.000000 4.000000 +#+end_example + +Observamos que el número de instancias disminuye considerablemente, hasta un máximo de 112, en el caso del /BI-RADS/. Aún así, los valores de la media y desviación estándar no se ven afectados de forma considerable. + +2. Imputar su valor con la media + +#+BEGIN_SRC python +df = read_csv(filepath_or_buffer="../data/mamografia.csv", na_values="?") +processed_df = process_na(df=df, action="fill") +print("DataFrame sin preprocesamiento: ") +print(df.describe()) +print("DataFrame sin preprocesamiento: ") +print(processed_df.describe()) +#+END_SRC + +#+RESULTS: +#+begin_example +DataFrame sin preprocesamiento: + BI-RADS Age Margin Density +count 961.000000 961.000000 961.000000 961.000000 +mean 4.296142 55.487448 2.796276 2.910734 +std 0.705555 14.442373 1.526880 0.365074 +min 0.000000 18.000000 1.000000 1.000000 +25% 4.000000 45.000000 1.000000 3.000000 +50% 4.000000 57.000000 3.000000 3.000000 +75% 5.000000 66.000000 4.000000 3.000000 +max 6.000000 96.000000 5.000000 4.000000 +DataFrame sin preprocesamiento: + BI-RADS Age Margin Density +count 961.000000 961.000000 961.000000 961.000000 +mean 4.296142 55.487448 2.796276 2.910734 +std 0.705555 14.442373 1.526880 0.365074 +min 0.000000 18.000000 1.000000 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