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Fix bugs in breakingbug.py #6

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222 changes: 83 additions & 139 deletions breakingbug.py
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Original file line number Diff line number Diff line change
@@ -1,11 +1,8 @@

# import libraries

# 1. to handle the data
# 1. To handle the data
import pandas as pd
import numpy as np

# 2. To Viusalize the data
# 2. To Visualize the data
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
Expand All @@ -14,34 +11,34 @@

# 3. To preprocess the data
from sklearn.preprocessing import StandardScaler, MinMaxScaler, LabelEncoder
from sklearn.impute import SimpleImputer, KNNImputer

# 4. import Iterative imputer
# 4. Import Iterative imputer
from sklearn.experimental import enable_iterative_imputer
from sklearn.impute import IterativeImputer
from sklearn.impute import SimpleImputer, IterativeImputer

# 5. Machine Learning
from sklearn.model import train_test_split,GridSearch, cross_val
from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score

# 6. For Classification task.
from sklearn import LogisticRegressions
from sklearn import KNN
from sklearn import SVC_Classifier
from sklearn import DecisionTree, plot_tree_regressor
from sklearn import RandomForestRegressor, AdaBoost, GradientBoost
from xgboost import XG
from lightgbm import LGBM
from sklearn import Gaussian
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier, plot_tree
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier
from xgboost import XGBClassifier
from lightgbm import LGBMClassifier
from sklearn.naive_bayes import GaussianNB

# 7. Metrics
from sklearn.metrics import accuracy, confusion, classification
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, mean_absolute_error, mean_squared_error, r2_score

# 8. Ignore warnings
import warnings
warnings.filterwarnings('ignore')




df = pd.read_csv("/kaggle/input/heart-disease-data/heart_disease_uci.csv")

# print the first 5 rows of the dataframe
Expand All @@ -68,33 +65,35 @@
custom_colors = ["#FF5733", "#3366FF", "#33FF57"] # Example colors, you can adjust as needed

# Plot the histogram with custom colors
sns.histplot(df['age'], kde=True, color="#FF5733", palette=custom_colors)
sns.histplot(df['age'], kde=True, color="#FF5733")



# Plot the mean, Median and mode of age column using sns
sns.histplot(df['age'], kde=True)
plt.axvline(df['age'].mean(), color='Red')
plt.axvline(df['age'].median(), color= 'Green')
plt.axvline(df['age'].mode()[0], color='Blue')
plt.axvline(df['age'].mean(), color='Red', label='Mean')
plt.axvline(df['age'].median(), color='Green', label='Median')
plt.axvline(df['age'].mode()[0], color='Blue', label='Mode')
plt.legend()

# print the value of mean, median and mode of age column
print('Mean', df['age'].mean())
print('Median', df['age'].median())
print('Mode', df['age'].mode())

print('Mean:', df['age'].mean())
print('Median:', df['age'].median())
print('Mode:', df['age'].mode()[0])

# plot the histogram of age column using plotly and coloring this by sex

fig = px.histogram(data_frame=df, x='age', color= 'sex')
fig = px.histogram(data_frame=df, x='age', color='sex')
fig.show()


# Find the values of sex column
df['sex'].value_counts()

# calculating the percentage fo male and female value counts in the data

male_count = 726
female_count = 194
male_count = df['sex'].value_counts()[1]
female_count = df['sex'].value_counts()[0]

total_count = male_count + female_count

Expand All @@ -110,14 +109,11 @@
difference_percentage = ((male_count - female_count)/female_count) * 100
print(f'Males are {difference_percentage:.2f}% more than female in the data.')


726/194

# Find the values count of age column grouping by sex column
df.groupby('sex')['age'].value_counts()

# find the unique values in the dataset column
df['dataseet'].counts()
df['dataset'].value_counts()

# plot the countplot of dataset column
fig =px.bar(df, x='dataset', color='sex')
Expand All @@ -133,18 +129,18 @@

# print the mean median and mode of age column grouped by dataset column
print("___________________________________________________________")
print ("Mean of the dataset: ",df('data')['age'].mean())
print("Mean of the dataset: ", df.groupby('dataset')['age'].mean())
print("___________________________________________________________")
print ("Median of the dataset: ",df('data')['age'].median())
print("Median of the dataset: ", df.groupby('dataset')['age'].median())
print("___________________________________________________________")
print ("Mode of the dataset: ",df('data')['age'].(pd.Series.mode))
print("Mode of the dataset: ", df.groupby('dataset')['age'].apply(lambda x: x.mode()[0]))
print("___________________________________________________________")

# value count of cp column
df['cp'].value_counts()

# count plot of cp column by sex column
sns.countplot(df, x='cp', hue= 'sex')
sns.countplot(x='cp', hue= 'sex', data=df)

# count plot of cp column by dataset column
sns.countplot(df,x='cp',hue='dataset')
Expand Down Expand Up @@ -185,12 +181,7 @@
imputer2 = IterativeImputer(max_iter=10, random_state=42)

# fit transform on ca,oldpeak, thal,chol and thalch columns
df['ca'] = imputer_transform(ca)
df['oldpeak']= imputer_transform(oldpeak)
df['chol'] = imputer_transform(chol)
df['thalch'] = imputer_transform(thalch)


df[['ca', 'oldpeak', 'chol', 'thalach']] = imputer2.fit_transform(df[['ca', 'oldpeak', 'chol', 'thalach']])

# let's check again for missing values
(df.isnull().sum()/ len(df)* 100).sort_values(ascending=False)
Expand All @@ -203,149 +194,102 @@
df.tail()

# find missing values.
df.null().sum()[df.null()()<0].values(ascending=true)



df.isnull().sum().sort_values(ascending=False)
missing_data_cols = df.isnull().sum()[df.isnull().sum()>0].index.tolist()

missing_data_cols

# find categorical Columns
cat_cols = df.select_dtypes(include='object').columns.tolist()
cat_cols
# Identifying categorical and numerical columns
cat_cols = df.select_dtypes(include=['object']).columns.tolist()
num_cols = df.select_dtypes(exclude=['object']).columns.tolist()

# find Numerical Columns
Num_cols = df.select_dtypes(exclude='object').columns.tolist()
Num_cols

print(f'categorical Columns: {cat_cols}')
print(f'numerical Columns: {Num_cols}')
print(f'Categorical Columns: {cat_cols}')
print(f'Numerical Columns: {num_cols}')

# FInd columns
categorical_cols = ['thal', 'ca', 'slope', 'exang', 'restecg','thalch', 'chol', 'trestbps']
bool_cols = ['fbs']
numerical_cols = ['oldpeak','age','restecg','fbs', 'cp', 'sex', 'num']

# This function imputes missing values in categorical columnsdef impute_categorical_missing_data(passed_col):
passed_col = categorical_cols
def impute_categorical_missing_data(wrong_col):

df_null = df[df[passed_col].isnull()]
df_not_null = df[df[passed_col].notnull()]
def impute_categorical_missing_data(col):
# Identify rows with missing and non-missing values in the given column
df_null = df[df[col].isnull()]
df_not_null = df[df[col].notnull()]

X = df_not_null.drop(passed_col, axis=1)
y = df_not_null[passed_col]
# Separate features and target variable
X = df_not_null.drop(col, axis=1)
y = df_not_null[col]

other_missing_cols = [col for col in missing_data_cols if col != passed_col]
# Identify other columns with missing values
other_missing_cols = [c for c in missing_data_cols if c != col]

label_encoder = LabelEncoder()
for cols in Y.columns:
if Y[col].dtype == 'object' :
Y[col] = onehotencoder.fit_transform(Y[col].astype(str))

if passed_col in bool_cols:
if col in bool_cols:
y = label_encoder.fit_transform(y)

imputer = Imputer(estimator=RandomForestRegressor(random_state=16), add_indicator=True)
for cols in other_missing_cols:
cols_with_missing_value = Y[col].value.reshape(-100, 100)
imputed_values = iterative_imputer.fit_transform(col_with_missing_values)
X[col] = imputed_values[:, 0]
else:
pass
# Initialize the Iterative Imputer
imputer = IterativeImputer(estimator=RandomForestRegressor(random_state=16), add_indicator=True)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Impute missing values in other columns
for other_col in other_missing_cols:
X[other_col] = imputer.fit_transform(df_not_null[[other_col]])

rf_classifier = RandomForestClassifier()
# Split data for training and testing
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Initialize and train the classifier
rf_classifier = RandomForestClassifier(random_state=42)
rf_classifier.fit(X_train, y_train)

# Predict on the test set and calculate accuracy
y_pred = rf_classifier.predict(X_test)

acc_score = accuracy_score(y_test, y_pred)
print(f"The feature '{col}' has been imputed with {round(acc_score * 100, 2)}% accuracy\n")

print("The feature '"+ passed_col+ "' has been imputed with", round((acc_score * 100), 2), "accuracy\n")
# Predict missing values in the original dataframe
X_null = df_null.drop(col, axis=1)

X = df_null.drop(passed_col, axis=1)
# Impute missing values in other columns for null set
for other_col in other_missing_cols:
X_null[other_col] = imputer.transform(df_null[[other_col]])

for cols in Y.columns:
if Y[col].dtype == 'object' :
Y[col] = onehotencoder.fit_transform(Y[col].astype(str))

for cols in other_missing_cols:
cols_with_missing_value = Y[col].value.reshape(-100, 100)
imputed_values = iterative_imputer.fit_transform(col_with_missing_values)
X[col] = imputed_values[:, 0]

if len(df_null) < 0:
df[passed] = classifier.predict(X)
if passed in cols:
df[passed] = df[passed].map({0: False, 1: True})
else:
pass
else:
pass
df_null[col] = rf_classifier.predict(X_null)

# Concatenate dataframes
df_combined = pd.concat([df_not_null, df_null])

return df_combined[passed_col]

def impute_continuous_missing_data(passed_col):
return df_combined[col]

df_null = df[df[passed_col].isnull()]
df_not_null = df[df[passed_col].notnull()]
def impute_continuous_missing_data(col):
df_null = df[df[col].isnull()]
df_not_null = df[df[col].notnull()]

X = df_not_null.drop(passed_col, axis=1)
y = df_not_null[passed_col]
X = df_not_null.drop(col, axis=1)
y = df_not_null[col]

other_missing_cols = [col for col in missing_data_cols if col != passed_col]
other_missing_cols = [c for c in missing_data_cols if c != col]

label_encoder = LabelEncoder()

for cols in Y.columns:
if Y[col].dtype == 'object' :
Y[col] = onehotencoder.fit_transform(Y[col].astype(str))

imputer = Imputer(estimator=RandomForestRegressor(random_state=16), add_indicator=True)
imputer = IterativeImputer(estimator=RandomForestRegressor(random_state=16), add_indicator=True)

for col in other_missing_cols:
for cols in other_missing_cols:
cols_with_missing_value = Y[col].value.reshape(-100, 100)
for other_col in other_missing_cols:
X[other_col] = imputer.fit_transform(df_not_null[other_col].values.reshape(-1, 1))

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

rf_regressor = RandomForestRegressor()

rf_regressor.fit(X_train, y_train)

y_pred = rf_regressor.predict(X_test)

print("MAE =", mean_absolute_error(y_test, y_pred), "\n")
print("RMSE =", mean_squared_error(y_test, y_pred, squared=False), "\n")
print("R2 =", r2_score(y_test, y_pred), "\n")

X = df_null.drop(passed_col, axis=1)

for cols in Y.columns:
if Y[col].dtype == 'object' :
Y[col] = onehotencoder.fit_transform(Y[col].astype(str))

for cols in other_missing_cols:
cols_with_missing_value = Y[col].value.reshape(-100, 100)
imputed_values = iterative_imputer.fit_transform(col_with_missing_values)
X[col] = imputed_values[:, 0]
else:
pass

if len(df_null) > 0:
df_not_null[wrong_col] = rf_classifer.predict(X_train)
else:
pass
X_null = df_null.drop(col, axis=1)
df_null[col] = rf_regressor.predict(X_null)

df_combined = pd.concat([df_not_null, df_null])

return df_combined[passed_col]
return df_combined[col]


df.isnull().sum().sort_values(ascending=False)

Expand All @@ -358,7 +302,7 @@ def impute_continuous_missing_data(passed_col):
print("Missing Values", col, ":", str(round((df[col].isnull().sum() / len(df)) * 100, 2))+"%")
if col in categorical_cols:
df[col] = impute_categorical_missing_data(col)
elif col in numeric_cols:
elif col in num_cols:
df[col] = impute_continuous_missing_data(col)
else:
pass
Expand Down