我的代码：
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
from sklearn.tree import DecisionTreeRegressor
# Set option to display all the rows and columns in the dataset. If there are more rows, adjust number accordingly.
pd.set_option('display.max_rows', 5000)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
# Pandas needs you to define the column as date before its imported and then call the column and define as a date
# hence this step.
date_col = ['Date']
df = pd.read_csv(
r'C:UsersharshDocumentsMy DreamDesktopMachine LearningAttempt1Historical DataConcat_Cleaned.csv'
, parse_dates=date_col, skiprows=0, low_memory=False)
# Converting/defining the columns
# Before you define column types, you need to fill all NaN with a value. We will be reconverting them later
df = df.fillna(101)
# Defining column types
convert_dict = {'League_Division': str,
'HomeTeam': str,
'AwayTeam': str,
'Full_Time_Home_Goals': int,
'Full_Time_Away_Goals': int,
'Full_Time_Result': str,
'Half_Time_Home_Goals': int,
'Half_Time_Away_Goals': int,
'Half_Time_Result': str,
'Attendance': int,
'Referee': str,
'Home_Team_Shots': int,
'Away_Team_Shots': int,
'Home_Team_Shots_on_Target': int,
'Away_Team_Shots_on_Target': int,
'Home_Team_Hit_Woodwork': int,
'Away_Team_Hit_Woodwork': int,
'Home_Team_Corners': int,
'Away_Team_Corners': int,
'Home_Team_Fouls': int,
'Away_Team_Fouls': int,
'Home_Offsides': int,
'Away_Offsides': int,
'Home_Team_Yellow_Cards': int,
'Away_Team_Yellow_Cards': int,
'Home_Team_Red_Cards': int,
'Away_Team_Red_Cards': int,
'Home_Team_Bookings_Points': float,
'Away_Team_Bookings_Points': float,
}
df = df.astype(convert_dict)
# Reverting the replace values step to get original dataframe and with the defined filetypes
df = df.replace('101', np.NAN, regex=True)
df = df.replace(101, np.NAN, regex=True)
# Clean dataset by dropping null rows
data = df.dropna(axis=0)
# Column that you want to predict = y
y = data.Full_Time_Home_Goals
# Columns that are inputted into the model to make predictions (dependants), Cannot be column y
features = ['HomeTeam', 'AwayTeam', 'Full_Time_Away_Goals', 'Full_Time_Result']
# Create X
X = data[features]
# Split into validation and training data
train_X, val_X, train_y, val_y = train_test_split(X, y, random_state=1)
# Specify Model
soccer_model = DecisionTreeRegressor(random_state=1)
# Define and train OneHotEncoder to transform numerical data to a numeric array
enc = OneHotEncoder(handle_unknown='ignore')
enc.fit(train_X, train_y)
transformed_train_X = enc.transform(train_X)
transformed_val_X = enc.transform(val_X)
# Fit Model
soccer_model.fit(transformed_train_X, train_y)
# Make validation predictions and calculate mean absolute error
val_predictions = soccer_model.predict(transformed_val_X)
val_mae = mean_absolute_error(val_predictions, val_y)
print("Validation MAE when not specifying max_leaf_nodes : {:,.5f}".format(val_mae))
# Using best value for max_leaf_nodes
data_model = DecisionTreeRegressor(max_leaf_nodes=100, random_state=1)
data_model.fit(transformed_train_X, train_y)
val_predictions = data_model.predict(transformed_val_X)
val_mae = mean_absolute_error(val_predictions, val_y)
print("Validation MAE for best value of max_leaf_nodes : {:,.5f}".format(val_mae))
# Build a Random Forest model and train it on all of X and y.
# To improve accuracy, create a new Random Forest model which you will train on all training data
rf_model_on_full_data = RandomForestRegressor()
# fit rf_model_on_full_data on all data from the training data
rf_model_on_full_data.fit(transformed_train_X, train_y)
# path to file you will use for predictions
date_col_n = ['Date']
test_data = pd.read_csv(
r'C:UsersharshDocumentsMy DreamDesktopMachine LearningAttempt1EPL_2021_Timetable.csv'
, parse_dates=date_col_n, skiprows=0, low_memory=False)
# columns = ['Home_Team', 'Away_Team']
test_data['ID'] = np.arange(len(test_data))
# Define columns we want to use for prediction
columns = ['Home_Team', 'Away_Team']
test_data = test_data[columns]
# Renaming Column Names to match with training dataset
test_data = test_data.rename({'Home_Team': 'HomeTeam', 'Away_Team': 'AwayTeam'}, axis=1)
# Adding NaN columns to dataset to match the training dataset
test_data['Full_Time_Result'] = np.nan
test_data['Full_Time_Away_Goals'] = np.nan
# Aligning dataframe to model defined
test_data_features = test_data[features]
# Filling all NA values as Encoder cannot handle nan values
df = test_data.fillna(1)
# Exploration
print(df)
# Define Y for Fitting
Y = df
# We need to encode and transform dataset so we have converted all nan to 1 and we are defining a new model as the
# valx values are confusing, we will use n_
train_n_X, val_n_X, train_n_y, val_n_y = train_test_split(Y, y, random_state=1)
enc.fit(train_n_X, train_n_y)
transformed_train_n_X = enc.transform(train_n_X)
transformed_val_n_X = enc.transform(val_n_X)
rf_model_on_full_data.fit(transformed_train_n_X, train_n_y)
错误：
Traceback (most recent call last): File "C:/Users/harsh/PycharmProjects/Learn Machine Learning/Attempt1/Working with soccer data attempt 1", line 137, in <module>
train_n_X, val_n_X, train_n_y, val_n_y = train_test_split(Y, y, random_state=1) File "C:UsersharshPycharmProjectsLearn Machine Learningvenvlibsite-packagessklearnmodel_selection_split.py", line 2127, in train_test_split
arrays = indexable(*arrays) File "C:UsersharshPycharmProjectsLearn Machine Learningvenvlibsite-packagessklearnutilsvalidation.py", line 292, in indexable
check_consistent_length(*result) File "C:UsersharshPycharmProjectsLearn Machine Learningvenvlibsite-packagessklearnutilsvalidation.py", line 256, in check_consistent_length
" samples: %r" % [int(l) for l in lengths]) ValueError: Found input variables with inconsistent numbers of samples: [380, 6643]
文件：concat_cleaned_dataset，test_data
问题：
- 在这种情况下，更改数组大小是否有助于适应
- 如果训练模型可以处理NaN值，为什么不从我的测试数据
我在这里犯的错误是根据train_n_X, val_n_X, train_n_y, val_n_y = train_test_split(Y, y, random_state=1)中的训练数据集维度拆分新数据集
正确的拆分应该根据新的数据集，因此，您的代码应该是：
train_n_X, val_n_X, train_n_y, val_n_y = train_test_split(Y, ny, random_state=1)
其中ny长度是根据新数据集长度
p.s.此代码需要修改，因为您的训练数据集维度与预测数据集不同。