<filename>193_xgboost_intro_using_wisconsin_dataset.py<gh_stars>1-10
#Ref: <NAME>

#Ref: <NAME>

pip install xgboost  

If you get an error "cannot import name 'XGBClassifier' from 'xgboost'"

first make sure you did not label your python file xgboost.py, creates conflict

If you still see this error...
try using conda to install.... 
https://anaconda.org/conda-forge/xgboost
conda install -c conda-forge xgboost

If doesn't work, try creating a new environment.
If all fails, try Google Colab or other local IDE. 

DATASET INFO
## '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 pandas as pd
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


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

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

# Encoding categorical data
from sklearn.preprocessing import LabelEncoder
labelencoder = LabelEncoder()
Y = labelencoder.fit_transform(y) # M=1 and B=0
#################################################################
#Define x and normalize values

#Define the independent variables. Let's also drop Gender, so we can normalize other data
X = df.drop(labels = ["Label", "ID"], 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=0.25, random_state=42)

##########################################################

#RANDOM FOREST, to compare against xgboost
from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier(n_estimators = 10, random_state = 42)
#
## Train the model on training data
#model.fit(X_train, y_train) #For sklearn no one hot encoding

# fit model to XGBOOST
import xgboost as xgb
model = xgb.XGBClassifier()

model.fit(X_train, y_train)


# Predicting the Test set results
y_pred = model.predict(X_test)
#y_pred = (y_pred > 0.5)

#Evaluate the classifier on test data
from sklearn.metrics import accuracy_score
accuracy = accuracy_score(y_test, y_pred)


print("Accuracy = ", (accuracy * 100.0), "%")

# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)

sns.heatmap(cm, annot=True)

