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MachineLearning.py
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MachineLearning.py
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from typing import final
import pandas as pd
from pandas import(
DataFrame,
Series
)
import seaborn as sns
from sklearn.base import BaseEstimator
from sklearn.preprocessing import QuantileTransformer
from sklearn.metrics import confusion_matrix, accuracy_score, precision_score
from sklearn.model_selection import GridSearchCV, cross_val_score, StratifiedKFold, learning_curve, train_test_split
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier, VotingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import SVC
@final
class MachineLearning:
'''
Functionality based on https://medium.com/geekculture/diabetes-prediction-using-machine-learning-python-23fc98125d8
'''
_df: DataFrame = None
_label: Series = None
def __init__(self, dataset: str='./dataset.csv', label: str = 'Outcome'):
data = pd.read_csv(dataset)
self._df = data
self._label = data[label]
def _col(self, col: str):
return self._df[col];
@property
def _cols(self):
return self._df.columns;
@final
def column(self, col: str):
return self._col(col)
@property
@final
def columns(self):
return self._cols
@final
def display_info(self):
return self._df.info()
@final
def description(self, attr: str = None, percentiles=[], include=None, exclude=None):
if attr != None:
return self._df[attr].describe(percentiles=percentiles, include=include, exclude=exclude)
else:
return self._df.describe(percentiles=percentiles, include=include, exclude=exclude)
@property
@final
def full_description(self):
return self.description().to_string()
@final
def display_corr(self, method: str = 'pearson'):
return self._df.corr(method=method)
# @final
def process_data_and_clean(self):
'''
Cleans and processes the dataset making it ready for modeling
'''
self._df = self._df.drop_duplicates()
# Correct missing values
for c in self._cols:
col = self._df[c]
if col.name != self._label.name and col.name != 'Pregnancies':
self._df[c] = col.replace(0, col.mean())
# cols = self._cols.to_list()
# # cols.remove(self._label.index)
# self._df.drop(self._df.loc[self.detect_outliers(0, cols)].index, inplace=True)
# # self._df = df
self._df = self._df.drop_duplicates()
return None
@final
def detect_outliers(self, n: int, features):
"""
Detect outliers from given list of features. It returns a list of the indices
according to the observations containing more than n outliers according
to the Tukey method
"""
import numpy as np
from collections import Counter
outlier_indices = []
# iterate over features(columns)
for col in features:
Q1 = np.percentile(self._df[col], 25)
Q3 = np.percentile(self._df[col], 75)
IQR = Q3 - Q1
# outlier step
outlier_step = 1.5 * IQR
# Determine a list of indices of outliers for feature col
outlier_list_col = self._df[(self._df[col] < Q1 - outlier_step) | (self._df[col] > Q3 + outlier_step )].index
# append the found outlier indices for col to the list of outlier indices
outlier_indices.extend(outlier_list_col)
# select observations containing more than n outliers
outlier_indices = Counter(outlier_indices)
multiple_outliers = list( k for k, v in outlier_indices.items() if v > n )
return multiple_outliers
def evaluate_models(self, models: "list[BaseEstimator]", x_train, y_train):
"""
Takes a list of models and returns chart of cross validation scores using mean accuracy
"""
# Cross validate model with Kfold stratified cross val
kfold = StratifiedKFold(n_splits = 10)
result = []
for model in models:
result.append(cross_val_score(estimator = model, X = x_train, y = y_train, scoring = "accuracy", cv = 10, n_jobs=4))
cv_means = []
cv_std = []
for cv_result in result:
cv_means.append(cv_result.mean())
cv_std.append(cv_result.std())
result_dataset = pd.DataFrame({
"CrossValMeans":cv_means,
"CrossValerrors": cv_std,
"Models":[
"LogisticRegression",
"DecisionTreeClassifier",
"AdaBoostClassifier",
"SVC",
"RandomForestClassifier",
"GradientBoostingClassifier",
"KNeighborsClassifier"
]
})
# Generate chart
bar = sns.barplot(x = "CrossValMeans", y = "Models", data = result_dataset, orient = "h")
bar.set_xlabel("Mean Accuracy")
bar.set_title("Cross validation scores")
return result_dataset
@final
def splitDataset(self, features, labels, test_size=0.30, random_state=7):
return train_test_split(features, labels, test_size=test_size, random_state=random_state)