39 KiB
39 KiB
Загрузка данных в DataFrame
In [2]:
import pandas as pd
df = pd.read_csv("../data/car_price_prediction.csv")
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df.head()
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Получение сведений о пропущенных данных
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print(df.isnull().sum())
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print(df.isnull().any())
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print(df["Levy"].unique())
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df["Levy"] = df["Levy"].replace({'-' : None})
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for i in df.columns:
null_rate = df[i].isnull().sum() / len(df) * 100
if null_rate > 0:
print(f"{i} процент пустых значений: {null_rate:.2f}%")
Заполнение пропущенных данных
In [9]:
df.fillna({"Levy": 0}, inplace=True)
for i in df.columns:
null_rate = df[i].isnull().sum() / len(df) * 100
if null_rate > 0:
print(f"{i} процент пустых значений: {null_rate:.2f}%")
Создание выборок данных
In [10]:
from sklearn.model_selection import train_test_split
def split_stratified_into_train_val_test(
df_input,
stratify_colname="y",
frac_train=0.6,
frac_val=0.15,
frac_test=0.25,
random_state=None,
):
"""
Splits a Pandas dataframe into three subsets (train, val, and test)
following fractional ratios provided by the user, where each subset is
stratified by the values in a specific column (that is, each subset has
the same relative frequency of the values in the column). It performs this
splitting by running train_test_split() twice.
Parameters
----------
df_input : Pandas dataframe
Input dataframe to be split.
stratify_colname : str
The name of the column that will be used for stratification. Usually
this column would be for the label.
frac_train : float
frac_val : float
frac_test : float
The ratios with which the dataframe will be split into train, val, and
test data. The values should be expressed as float fractions and should
sum to 1.0.
random_state : int, None, or RandomStateInstance
Value to be passed to train_test_split().
Returns
-------
df_train, df_val, df_test :
Dataframes containing the three splits.
"""
if frac_train + frac_val + frac_test != 1.0:
raise ValueError(
"fractions %f, %f, %f do not add up to 1.0"
% (frac_train, frac_val, frac_test)
)
if stratify_colname not in df_input.columns:
raise ValueError("%s is not a column in the dataframe" % (stratify_colname))
X = df_input # Contains all columns.
y = df_input[
[stratify_colname]
] # Dataframe of just the column on which to stratify.
# Split original dataframe into train and temp dataframes.
df_train, df_temp, y_train, y_temp = train_test_split(
X, y, stratify=y, test_size=(1.0 - frac_train), random_state=random_state
)
# Split the temp dataframe into val and test dataframes.
relative_frac_test = frac_test / (frac_val + frac_test)
df_val, df_test, y_val, y_test = train_test_split(
df_temp,
y_temp,
stratify=y_temp,
test_size=relative_frac_test,
random_state=random_state,
)
assert len(df_input) == len(df_train) + len(df_val) + len(df_test)
return df_train, df_val, df_test
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print(df.Gear_box_type.unique())
data = df[
[
"Price",
"Gear_box_type",
]
].copy()
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df_train, df_val, df_test = split_stratified_into_train_val_test(
data,
stratify_colname="Gear_box_type",
frac_train=0.60,
frac_val=0.20,
frac_test=0.20,
)
print("Обучающая выборка: ", df_train.shape)
print(df_train.Gear_box_type.value_counts())
print("Контрольная выборка: ", df_val.shape)
print(df_val.Gear_box_type.value_counts())
print("Тестовая выборка: ", df_test.shape)
print(df_test.Gear_box_type.value_counts())
Выборка с избытком (oversampling)
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from imblearn.over_sampling import ADASYN
ada = ADASYN()
print("Обучающая выборка: ", df_train.shape)
print(df_train.Gear_box_type.value_counts())
X_resampled, y_resampled = ada.fit_resample(df_train, df_train["Gear_box_type"])
df_train_adasyn = pd.DataFrame(X_resampled)
print("Обучающая выборка после oversampling: ", df_train_adasyn.shape)
print(df_train_adasyn.Gear_box_type.value_counts())