From f49764bf41d3177e5793ef74327420bced98e6ef Mon Sep 17 00:00:00 2001 From: coolneng Date: Wed, 9 Dec 2020 14:43:47 +0100 Subject: [PATCH] Separate each part of the lab in a folder --- docs/assets/attribute's_correlation.png | Bin 0 -> 66201 bytes docs/assets/confusion_matrix.png | Bin 0 -> 26054 bytes docs/assets/roc_auc_curve.png | Bin 88666 -> 65770 bytes src/{ => P1}/preprocessing.py | 0 src/{ => P1}/processing.py | 0 src/P2/preprocessing.py | 49 ++++++++++++++++++++++++ 6 files changed, 49 insertions(+) create mode 100644 docs/assets/attribute's_correlation.png create mode 100644 docs/assets/confusion_matrix.png rename src/{ => P1}/preprocessing.py (100%) rename src/{ => P1}/processing.py (100%) create mode 100644 src/P2/preprocessing.py diff --git a/docs/assets/attribute's_correlation.png b/docs/assets/attribute's_correlation.png new file mode 100644 index 0000000000000000000000000000000000000000..ba5d426f743687a30b62eee4fe53260a1efea68d GIT binary patch literal 66201 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"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

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