tutorial76-Random Forest_breast_cancer.py

# https://youtu.be/D8repXHkKdk

"""
https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)
## 'data.frame':    569 obs. of  31 variables:
##  $ diagnosis              : Factor w/ 2 levels "Benign","Malignant": 2 2 2 2 2 2 2 2 2 2 ...
##  $ radius_mean            : num  18 20.6 19.7 11.4 20.3 ...
##  $ texture_mean           : num  10.4 17.8 21.2 20.4 14.3 ...
##  $ perimeter_mean         : num  122.8 132.9 130 77.6 135.1 ...
##  $ area_mean              : num  1001 1326 1203 386 1297 ...
##  $ smoothness_mean        : num  0.1184 0.0847 0.1096 0.1425 0.1003 ...
##  $ compactness_mean       : num  0.2776 0.0786 0.1599 0.2839 0.1328 ...
##  $ concavity_mean         : num  0.3001 0.0869 0.1974 0.2414 0.198 ...
##  $ concave.points_mean    : num  0.1471 0.0702 0.1279 0.1052 0.1043 ...
##  $ symmetry_mean          : num  0.242 0.181 0.207 0.26 0.181 ...
##  $ fractal_dimension_mean : num  0.0787 0.0567 0.06 0.0974 0.0588 ...
##  $ radius_se              : num  1.095 0.543 0.746 0.496 0.757 ...
##  $ texture_se             : num  0.905 0.734 0.787 1.156 0.781 ...
##  $ perimeter_se           : num  8.59 3.4 4.58 3.44 5.44 ...
##  $ area_se                : num  153.4 74.1 94 27.2 94.4 ...
##  $ smoothness_se          : num  0.0064 0.00522 0.00615 0.00911 0.01149 ...
##  $ compactness_se         : num  0.049 0.0131 0.0401 0.0746 0.0246 ...
##  $ concavity_se           : num  0.0537 0.0186 0.0383 0.0566 0.0569 ...
##  $ concave.points_se      : num  0.0159 0.0134 0.0206 0.0187 0.0188 ...
##  $ symmetry_se            : num  0.03 0.0139 0.0225 0.0596 0.0176 ...
##  $ fractal_dimension_se   : num  0.00619 0.00353 0.00457 0.00921 0.00511 ...
##  $ radius_worst           : num  25.4 25 23.6 14.9 22.5 ...
##  $ texture_worst          : num  17.3 23.4 25.5 26.5 16.7 ...
##  $ perimeter_worst        : num  184.6 158.8 152.5 98.9 152.2 ...
##  $ area_worst             : num  2019 1956 1709 568 1575 ...
##  $ smoothness_worst       : num  0.162 0.124 0.144 0.21 0.137 ...
##  $ compactness_worst      : num  0.666 0.187 0.424 0.866 0.205 ...
##  $ concavity_worst        : num  0.712 0.242 0.45 0.687 0.4 ...
##  $ concave.points_worst   : num  0.265 0.186 0.243 0.258 0.163 ...
##  $ symmetry_worst         : num  0.46 0.275 0.361 0.664 0.236 ...
##  $ fractal_dimension_worst: num  0.1189 0.089 0.0876 0.173 0.0768 ...


"""

import numpy as np
import cv2
import pandas as pd
from matplotlib import pyplot as plt
import seaborn as sns
 
df = pd.read_csv("data/wisconsin_breast_cancer_dataset.csv")

print(df.describe().T)  #Values need to be normalized before fitting. 


print(df.isnull().sum())
#df = df.dropna()

#Rename Dataset to Label to make it easy to understand
df = df.rename(columns={'Diagnosis':'Label'})
print(df.dtypes)

#Understand the data 
sns.countplot(x="Label", data=df) #M - malignant   B - benign

sns.distplot(df['radius_mean'], kde=False)

print(df.corr())

corrMatrix = df.corr()
fig, ax = plt.subplots(figsize=(10,10))         # Sample figsize in inches
#sns.heatmap(df.iloc[:, 1:6:], annot=True, linewidths=.5, ax=ax)
sns.heatmap(corrMatrix, annot=False, linewidths=.5, ax=ax)


#Replace categorical values with numbers
df['Label'].value_counts()

categories = {"B":1, "M":2}
df['Label'] = df['Label'].replace(categories)


#Define the dependent variable that needs to be predicted (labels)
Y = df["Label"].values

#Define the independent variables. Let's also drop Gender, so we can normalize other data
X = df.drop(labels = ["Label", "ID"], axis=1) 
features_list = list(X.columns)  #List features so we can rank them later.
#from sklearn.preprocessing import normalize
#X = normalize(X, axis=1)

from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
scaler.fit(X)
X = scaler.transform(X)

#Split data into train and test to verify accuracy after fitting the model. 
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=42)

#RANDOM FOREST
from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier(n_estimators = 25, random_state = 42)

# Train the model on training data
model.fit(X_train, y_train)


prediction = model.predict(X_test)

from sklearn import metrics
print ("Accuracy = ", metrics.accuracy_score(y_test, prediction))


#Confusion Matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, prediction)
print(cm)

#Print individual accuracy values for each class, based on the confusion matrix
print("Benign = ", cm[0,0] / (cm[0,0]+cm[1,0]))
print("Malignant = ",   cm[1,1] / (cm[0,1]+cm[1,1]))


#importances = list(model_RF.feature_importances_)
feature_imp = pd.Series(model.feature_importances_, index=features_list).sort_values(ascending=False)
print(feature_imp)