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preprocessing.py
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preprocessing.py
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#!/usr/bin/env python
# coding: utf-8
# In[ ]:
import pandas as pd
from ydata_profiling import ProfileReport
import numpy as np
import random
import json
import os
from sqlalchemy import create_engine
from sqlalchemy.orm import Session
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score
from sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score
from mlxtend.plotting import plot_confusion_matrix
import warnings
warnings.filterwarnings("ignore")
from sql_lib import sql
from imblearn.over_sampling import SMOTE
from sklearn.utils import resample
from sklearn.ensemble import RandomForestClassifier
import tensorflow as tf
from tensorflow.keras import regularizers
training_raw = sql("""
SELECT * FROM canciones_features
LEFT JOIN canciones_overview ON
canciones_overview.id = canciones_features.id
""")
prediction_raw = sql("SELECT * FROM canciones_2023")
def create_target_variable(df):
"""
Returns a DataFrame of countries predicted to make it in the top five of Eurovision 2023.
Parameters:
-----------
model : sklearn estimator object
The trained machine learning model to predict the top five countries.
data_2023 : pandas DataFrame
The data for countries participating in Eurovision 2023.
Returns:
--------
pandas DataFrame
A DataFrame containing the countries predicted to make it in the top five.
"""
target_array = []
for i in df["final_place"]:
if i < 6:
target = 1
else:
target = 0
target_array.append(target)
return np.array(target_array)
def statistics(model, X_test, y_test):
"""
Calculate and print various statistics (accuracy, precision, recall, and F1 score)
for a given classification model and test data. Also, plot a confusion matrix.
Args:
model: a trained classification model with a predict() method.
X_test: input data to test the model on.
y_test: true labels corresponding to the input data.
Returns:
None
"""
from sklearn.metrics import confusion_matrix
y_pred = model.predict(X_test)
confusion_matrix = confusion_matrix(y_test, y_pred)
accuracy = accuracy_score(y_test, y_pred)
print('Accuracy: %f' % accuracy)
precision = precision_score(y_test, y_pred)
print('Precision: %f' % precision)
recall = recall_score(y_test, y_pred)
print('Recall: %f' % recall)
f1 = f1_score(y_test, y_pred)
print('F1 score: %f' % f1)
plot_confusion_matrix(conf_mat=confusion_matrix, figsize=(6, 6), cmap=plt.cm.RdPu)
plt.suptitle("Confusion matrix")
plt.show()
def top_2023(model, data_2023):
"""
Create target variable array from final_place column of a dataframe.
Args:
- df (pd.DataFrame): Dataframe containing the final_place column.
Returns:
- np.ndarray: Array of binary values representing whether the country finished
in the top 5 (1) or not (0).
"""
prediccion_m1_2023= model.predict(data_2023)
paises= sql("""SELECT country FROM canciones_2023""")
top = pd.concat([paises, pd.DataFrame(prediccion_m1_2023)], axis=1)
print(f"Model predicts {len(top[top[0] == 1])} countries will make it in the top five")
return top[top[0] == 1]
def nn_statistics(model, X_test, y_test):
"""
Calculate and print various statistics (accuracy, precision, recall, and F1 score)
for a given classification model and test data. Also, plot a confusion matrix.
Args:
model: a trained classification model with a predict() method.
X_test: input data to test the model on.
y_test: true labels corresponding to the input data.
Returns:
None
"""
from sklearn.metrics import confusion_matrix
y_pred = model.predict(X_test).flatten()
y_pred = [np.round(i).astype(int) for i in y_pred]
confusion_matrix = confusion_matrix(y_test, y_pred)
plot_confusion_matrix(conf_mat=confusion_matrix, figsize=(6, 6), cmap=plt.cm.RdPu)
plt.suptitle("Confusion matrix")
plt.show()
accuracy = accuracy_score(y_test, y_pred)
print('Accuracy: %f' % accuracy)
precision = precision_score(y_test, y_pred)
print('Precision: %f' % precision)
recall = recall_score(y_test, y_pred)
print('Recall: %f' % recall)
f1 = f1_score(y_test, y_pred)
print('F1 score: %f' % f1)