DecisionTrees

Decision trees - Arbori de decizie

Problema if-uri

alt text

Task: De implementat un algoritm cu if-uri conform schemei de mai sus

def decision(age, pizza, hamburger, exercise):
  if age < 30:
    elif

Ș### Problema Decision trees

(e imagine aici muahahahaha)

alt text

TODO:

# TODO:
from sklearn.datasets import load_iris
from sklearn import tree
X, y = load_iris(return_X_y=True)

import matplotlib.pyplot as plt
plt.plot(X[:, 2], 'o')

[<matplotlib.lines.Line2D at 0x7f8682238c88>]

png

# TODO: de facut DecisionTreeClassifier
#

from sklearn.tree import DecisionTreeClassifier, plot_tree

plot_tree(clf)

Clasificarea setului de date despre ciuperci - practica

Acest set de date include descrierile eșantioanelor ipotetice corespunzătoare a 23 de specii de ciuperci. Fiecare specie este identificată ca fiind definitiv comestibilă, definitiv otrăvitoare sau de comestibilitate necunoscută și nu este recomandată. Această ultimă clasă a fost combinată cu cea otrăvitoare. Ghidul precizează clar că nu există o regulă simplă pentru a determina comestibilitatea unei ciuperci;

descrierea coloanelor

• cap-shape: bell=b,conical=c,convex=x,flat=f, knobbed=k,sunken=s

• cap-surface: fibrous=f,grooves=g,scaly=y,smooth=s

• ....

• habitat: grasses=g,leaves=l,meadows=m,paths=p,urban=u,waste=w,woods=d

• more info here https://www.kaggle.com/uciml/mushroom-classification

Vom prezice coloana "class", care poate avea 2 valori:

• 'e' - edible (comestibil) sau

• 'p' - 'poisonous' (otravitor)

Importam cateva librarii necesare

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

Încărcăm setul de date

# https://www.kaggle.com/uciml/mushroom-classification
# DATASET_PATH = 'mushrooms.csv'

DATASET_PATH = 'https://girlsgoitpublic.z6.web.core.windows.net/mushrooms.csv'

data = pd.read_csv(DATASET_PATH)
data.head()

class

cap-shape

cap-surface

cap-color

bruises

odor

gill-attachment

gill-spacing

gill-size

gill-color

stalk-shape

stalk-root

stalk-surface-above-ring

stalk-surface-below-ring

stalk-color-above-ring

stalk-color-below-ring

veil-type

veil-color

ring-number

ring-type

spore-print-color

population

habitat

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data.describe()

	class	cap-shape	cap-surface	cap-color	bruises	odor	gill-attachment	gill-spacing	gill-size	gill-color	stalk-shape	stalk-root	stalk-surface-above-ring	stalk-surface-below-ring	stalk-color-above-ring	stalk-color-below-ring	veil-type	veil-color	ring-number	ring-type	spore-print-color	population	habitat
count	8124	8124	8124	8124	8124	8124	8124	8124	8124	8124	8124	8124	8124	8124	8124	8124	8124	8124	8124	8124	8124	8124	8124
unique	2	6	4	10	2	9	2	2	2	12	2	5	4	4	9	9	1	4	3	5	9	6	7
top	e	x	y	n	f	n	f	c	b	b	t	b	s	s	w	w	p	w	o	p	w	v	d
freq	4208	3656	3244	2284	4748	3528	7914	6812	5612	1728	4608	3776	5176	4936	4464	4384	8124	7924	7488	3968	2388	4040	3148

data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 8124 entries, 0 to 8123
Data columns (total 23 columns):
 #   Column                    Non-Null Count  Dtype 
---  ------                    --------------  ----- 
 0   class                     8124 non-null   object
 1   cap-shape                 8124 non-null   object
 2   cap-surface               8124 non-null   object
 3   cap-color                 8124 non-null   object
 4   bruises                   8124 non-null   object
 5   odor                      8124 non-null   object
 6   gill-attachment           8124 non-null   object
 7   gill-spacing              8124 non-null   object
 8   gill-size                 8124 non-null   object
 9   gill-color                8124 non-null   object
 10  stalk-shape               8124 non-null   object
 11  stalk-root                8124 non-null   object
 12  stalk-surface-above-ring  8124 non-null   object
 13  stalk-surface-below-ring  8124 non-null   object
 14  stalk-color-above-ring    8124 non-null   object
 15  stalk-color-below-ring    8124 non-null   object
 16  veil-type                 8124 non-null   object
 17  veil-color                8124 non-null   object
 18  ring-number               8124 non-null   object
 19  ring-type                 8124 non-null   object
 20  spore-print-color         8124 non-null   object
 21  population                8124 non-null   object
 22  habitat                   8124 non-null   object
dtypes: object(23)
memory usage: 1.4+ MB

• Ce concluzii deducem?

• Ce tipuri de date avem?

• Avem date lipsa?

Vizualizam datele

# ce conditii if putem crea aici?
plt.scatter(data['odor'], data['class'])
plt.title('Odor vs class')
plt.xlabel('Odor')
plt.ylabel('Class')
# plt.legend()
plt.show()

png

Preprocesăm datele

Date categoriale - Encoding

# Exemplu cu LabelEncoder:
from sklearn.preprocessing import LabelEncoder

lista_categorii = ['edible', 'poisonous']

le = LabelEncoder()

le.fit(lista_categorii)

lista_categorii_encoded = le.transform(lista_categorii)

np.unique(lista_categorii_encoded)

array([0, 1])

#ce va returna 
le.transform(['edible', 'poisonous','poisonous'])

array([0, 1, 1])

mapare = dict(poisonous=1, edible=0)# {'edible': 0, 'poisonous': 1}
mapat = []
for i in ['edible', 'poisonous','poisonous']:
  mapat.append(mapare[i])
mapat

[0, 1, 1]

mai intai vom face o copie a datelor

data_encoded = data.copy()

# from sklearn.tree import DecisionTreeClassifier
from sklearn.preprocessing import LabelEncoder


# Transformam toate coloanele in date numerice
for (columnName, columnData) in data_encoded.iteritems():
  le = LabelEncoder()

  le.fit(columnData)

  data_encoded[columnName] = le.transform(columnData)


# print(np.unique(data_encoded['class']))
data_encoded.head()

	class	cap-shape	cap-surface	cap-color	bruises	odor	gill-attachment	gill-spacing	gill-size	gill-color	stalk-shape	stalk-root	stalk-surface-above-ring	stalk-surface-below-ring	stalk-color-above-ring	stalk-color-below-ring	veil-type	veil-color	ring-number	ring-type	spore-print-color	population	habitat
0	1	5	2	4	1	6	1	0	1	4	0	3	2	2	7	7	0	2	1	4	2	3	5
1	0	5	2	9	1	0	1	0	0	4	0	2	2	2	7	7	0	2	1	4	3	2	1
2	0	0	2	8	1	3	1	0	0	5	0	2	2	2	7	7	0	2	1	4	3	2	3
3	1	5	3	8	1	6	1	0	1	5	0	3	2	2	7	7	0	2	1	4	2	3	5
4	0	5	2	3	0	5	1	1	0	4	1	3	2	2	7	7	0	2	1	0	3	0	1

data_encoded.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 8124 entries, 0 to 8123
Data columns (total 23 columns):
 #   Column                    Non-Null Count  Dtype
---  ------                    --------------  -----
 0   class                     8124 non-null   int64
 1   cap-shape                 8124 non-null   int64
 2   cap-surface               8124 non-null   int64
 3   cap-color                 8124 non-null   int64
 4   bruises                   8124 non-null   int64
 5   odor                      8124 non-null   int64
 6   gill-attachment           8124 non-null   int64
 7   gill-spacing              8124 non-null   int64
 8   gill-size                 8124 non-null   int64
 9   gill-color                8124 non-null   int64
 10  stalk-shape               8124 non-null   int64
 11  stalk-root                8124 non-null   int64
 12  stalk-surface-above-ring  8124 non-null   int64
 13  stalk-surface-below-ring  8124 non-null   int64
 14  stalk-color-above-ring    8124 non-null   int64
 15  stalk-color-below-ring    8124 non-null   int64
 16  veil-type                 8124 non-null   int64
 17  veil-color                8124 non-null   int64
 18  ring-number               8124 non-null   int64
 19  ring-type                 8124 non-null   int64
 20  spore-print-color         8124 non-null   int64
 21  population                8124 non-null   int64
 22  habitat                   8124 non-null   int64
dtypes: int64(23)
memory usage: 1.4 MB

https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html

Separam datele de antrenare de clase

printam numele coloanelor mai intai

print(data_encoded.columns)

Index(['class', 'cap-shape', 'cap-surface', 'cap-color', 'bruises', 'odor',
       'gill-attachment', 'gill-spacing', 'gill-size', 'gill-color',
       'stalk-shape', 'stalk-root', 'stalk-surface-above-ring',
       'stalk-surface-below-ring', 'stalk-color-above-ring',
       'stalk-color-below-ring', 'veil-type', 'veil-color', 'ring-number',
       'ring-type', 'spore-print-color', 'population', 'habitat'],
      dtype='object')

• X-ul va contine features (caracteristici) - toate coloanele in afara de clase

• Y-ul va contine doar denumirile claselor

# X = data_encoded[['cap-shape', 'cap-surface', 'cap-color', 'bruises', 'odor',
#        'gill-attachment', 'gill-spacing', 'gill-size', 'gill-color',
#        'stalk-shape', 'stalk-root', 'stalk-surface-above-ring',
#        'stalk-surface-below-ring', 'stalk-color-above-ring',
#        'stalk-color-below-ring', 'veil-type', 'veil-color', 'ring-number',
#        'ring-type', 'spore-print-color', 'population', 'habitat']]
Y = data_encoded[['class']].copy()
X = data_encoded.drop(columns=['class'])
# Y = data_encoded['class'].values.flatten() ## to_numpy()

X.head()

	cap-shape	cap-surface	cap-color	bruises	odor	gill-attachment	gill-spacing	gill-size	gill-color	stalk-shape	stalk-root	stalk-surface-above-ring	stalk-surface-below-ring	stalk-color-above-ring	stalk-color-below-ring	veil-type	veil-color	ring-number	ring-type	spore-print-color	population	habitat
0	5	2	4	1	6	1	0	1	4	0	3	2	2	7	7	0	2	1	4	2	3	5
1	5	2	9	1	0	1	0	0	4	0	2	2	2	7	7	0	2	1	4	3	2	1
2	0	2	8	1	3	1	0	0	5	0	2	2	2	7	7	0	2	1	4	3	2	3
3	5	3	8	1	6	1	0	1	5	0	3	2	2	7	7	0	2	1	4	2	3	5
4	5	2	3	0	5	1	1	0	4	1	3	2	2	7	7	0	2	1	0	3	0	1

Y.head()

	class
0	1
1	0
2	0
3	1
4	0

Train test split

from sklearn.model_selection import train_test_split

X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.33, random_state=42)

print("X_train shape:", X_train.shape)
print("Y_train shape:", Y_train.shape)

print("X_test shape:", X_test.shape)
print("Y_test shape:", Y_test.shape)

X_train shape: (5443, 22)
Y_train shape: (5443, 1)
X_test shape: (2681, 22)
Y_test shape: (2681, 1)

https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html

Construim modelul

from sklearn.tree import DecisionTreeClassifier

clf = DecisionTreeClassifier(max_depth=3)
print(clf)

DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',
                       max_depth=3, max_features=None, max_leaf_nodes=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, presort='deprecated',
                       random_state=None, splitter='best')

Antrenam modoelul

clf.fit(X_train, Y_train)

DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',
                       max_depth=3, max_features=None, max_leaf_nodes=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, presort='deprecated',
                       random_state=None, splitter='best')

Plot decision tree

from sklearn.tree import DecisionTreeClassifier, plot_tree
plt.figure(figsize=(20, 20))
plot_tree(clf)

NameError                                 Traceback (most recent call last)

<ipython-input-1-34989c638434> in <module>()
      1 from sklearn.tree import DecisionTreeClassifier, plot_tree
----> 2 plt.figure(figsize=(20, 20))
      3 plot_tree(clf)


NameError: name 'plt' is not defined

Evaluarea modelului

Y_predict = clf.predict(X_test)

Y_predict.sum(), Y_predict.shape

(1330, (2681,))

from sklearn.metrics import accuracy_score, f1_score, recall_score

print("Accuracy score:", accuracy_score(Y_test, Y_predict))
print("F1 score:", f1_score(Y_test, Y_predict))
print("Recall score:", recall_score(Y_test, Y_predict))

Accuracy score: 0.9772472957851548
F1 score: 0.97683251044436
Recall score: 0.9869531849577897

image.png

image.png

from sklearn.metrics import confusion_matrix

confusion_matrix(Y_test, Y_predict)

array([[1334,   44],
       [  17, 1286]])

# calculati acuraterea folosind numpy

from sklearn.metrics import classification_report

target_names = ['edible', 'poisonous']
print(classification_report(Y_test, Y_predict, target_names=target_names))

              precision    recall  f1-score   support

      edible       0.99      0.97      0.98      1378
   poisonous       0.97      0.99      0.98      1303

    accuracy                           0.98      2681
   macro avg       0.98      0.98      0.98      2681
weighted avg       0.98      0.98      0.98      2681

Cross-validation score

from sklearn.model_selection import cross_val_score

cross_val_score(clf, X_test, Y_test, cv=3)

array([0.9753915 , 0.9753915 , 0.95296753])

image.png

from sklearn.model_selection import KFold

kf = KFold(n_splits=3)
kf.get_n_splits(X_train)
#X_train, Y_train
print(kf)
fold = 1

for train_index, test_index in kf.split(X_train):
    print("TRAIN:", train_index, "TEST:", test_index)
    x_train, x_test = X_train.to_numpy()[train_index], X_train.to_numpy()[test_index]
    y_train, y_test = Y_train.to_numpy()[train_index], Y_train.to_numpy()[test_index]

    clf = DecisionTreeClassifier(max_depth=4)
    clf.fit(x_train, y_train)

    Y_predict = clf.predict(x_test)
    print('Fold {}'.format(fold))
    fold+=1
    print("Accuracy score:", accuracy_score(y_test, Y_predict))
    print("F1 score:", f1_score(y_test, Y_predict))
    print("Recall score:", recall_score(y_test, Y_predict))

KFold(n_splits=3, random_state=None, shuffle=False)
TRAIN: [1815 1816 1817 ... 5440 5441 5442] TEST: [   0    1    2 ... 1812 1813 1814]
Fold 1
Accuracy score: 0.9696969696969697
F1 score: 0.9684089603676048
Recall score: 0.9429530201342282
TRAIN: [   0    1    2 ... 5440 5441 5442] TEST: [1815 1816 1817 ... 3626 3627 3628]
Fold 2
Accuracy score: 0.9746416758544653
F1 score: 0.9735327963176065
Recall score: 0.9848661233993015
TRAIN: [   0    1    2 ... 3626 3627 3628] TEST: [3629 3630 3631 ... 5440 5441 5442]
Fold 3
Accuracy score: 0.9779492833517089
F1 score: 0.9770114942528736
Recall score: 0.9883720930232558

Tuning - ajustarea modelului

Parametri

Ce parametri avem pentru DecisionTreeClassifier?

• criterion {“gini”, “entropy”}, default=”gini” The function to measure the quality of a split

• splitter {“best”, “random”}, default=”best” The strategy used to choose the split at each node. Supported strategies are “best” to choose the best split and “random” to choose the best random split.

• max_depthint, default=None The maximum depth of the tree. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples

• random_stateint*, RandomState instance, default=None Controls the randomness of the estimator

• max_leaf_nodesint, default=None Grow a tree with max_leaf_nodes in best-first fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes.

• class_weightdict, list of dict or “balanced”, default=None Weights associated with classes in the form {class_label: weight}. If None, all classes are supposed to have weight one. For multi-output problems, a list of dicts can be provided in the same order as the columns of y.

Mai multi parametri aici: https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html

Train test validation split

#train, validate, test = np.split(df.sample(frac=1), [int(.6*len(df)), int(.8*len(df))])
dataX, dataY = X, Y

train_ratio = 0.75
validation_ratio = 0.15
test_ratio = 0.10

# train is now 75% of the entire data set
# the _junk suffix means that we drop that variable completely
x_train, x_test, y_train, y_test = train_test_split(dataX, dataY, test_size=1 - train_ratio)

# test is now 10% of the initial data set
# validation is now 15% of the initial data set
x_val, x_test, y_val, y_test = train_test_split(x_test, y_test, test_size=test_ratio/(test_ratio + validation_ratio)) 

print(x_train.shape, x_val.shape, x_test.shape)

(6093, 22) (1218, 22) (813, 22)

Ajustam modelul cu diversi parametri

import random
from sklearn.metrics import accuracy_score, f1_score, recall_score

# Exemplu cu train test validation for ... sa incercam mai multi parametri

random_states_list = [0,1, 2]
# If None then unlimited number of leaf nodes.
# max_leaf_nodes_list = [None, 5, 10, 100]
# max_depth_list = [1, 2, 20, 50]

acc_scores = []
f1_scores = []
recall_scores = []
Y_predicts = []

for random_state in random_states_list:
  max_leaf_nodes = None
  max_depth = 4
  # for max_leaf_nodes in max_leaf_nodes_list:
    # for max_depth in max_depth_list:

  clf = DecisionTreeClassifier(random_state=random_state, max_leaf_nodes=max_leaf_nodes, max_depth=max_depth)
  clf.fit(x_train, y_train)

  Y_predict = clf.predict(x_val)
  Y_predicts.append(Y_predict)

  # print("Accuracy score:", accuracy_score(Y_test, Y_predict))
  acc_scores.append(accuracy_score(y_val, Y_predict))
  # print("F1 score:", f1_score(Y_test, Y_predict))
  f1_scores.append(f1_score(y_val, Y_predict))
  # print("Recall score:", recall_score(Y_test, Y_predict))
  recall_scores.append(recall_score(y_val, Y_predict))

# TODO: subplots
plt.title("Cum influenteaza random state asupra accuracy")


plt.title("Cum influenteaza random state asupra recall")
plt.plot(random_states_list, acc_scores, 'x')
plt.xlabel('Random state')
plt.ylabel('Accuracy score')

Text(0, 0.5, 'Accuracy score')

png

max_leaf_nodes_list = [None] + list(range(4,30))

acc_scores = []
f1_scores = []
recall_scores = []


for max_leaf_nodes in max_leaf_nodes_list:
      clf = DecisionTreeClassifier(max_leaf_nodes=max_leaf_nodes)
      clf.fit(x_train, y_train)

      Y_predict = clf.predict(x_val)
      Y_predicts.append(Y_predict)

      # print("Accuracy score:", accuracy_score(Y_test, Y_predict))
      acc_scores.append(accuracy_score(y_val, Y_predict))
      # print("F1 score:", f1_score(Y_test, Y_predict))
      f1_scores.append(f1_score(y_val, Y_predict))
      # print("Recall score:", recall_score(Y_test, Y_predict))
      recall_scores.append(recall_score(y_val, Y_predict))

# max_leaf_nodes_list[0] = -1

plt.title("Cum influenteaza nr de frunze asupra accuracy")
plt.plot(max_leaf_nodes_list, acc_scores, 'x')
# plt.plot(max_leaf_nodes_list, f1_scores, 'o')
# plt.plot(max_leaf_nodes_list, recall_scores, 'x')
plt.xlabel('Max leaf nodes')
plt.ylabel('Accuracy score')

Text(0, 0.5, 'Accuracy score')

png

acc_scores = []
f1_scores = []
recall_scores = []

# max_depth_list = [1, 2, 30, 50]

# for max_depth in max_depth_list:
for max_depth in range(1, 20, 2):

      clf = DecisionTreeClassifier(max_depth=max_depth)
      clf.fit(x_train, y_train)

      Y_predict = clf.predict(x_val)
      Y_predicts.append(Y_predict)

      # print("Accuracy score:", accuracy_score(Y_test, Y_predict))
      acc_scores.append(accuracy_score(y_val, Y_predict))
      # print("F1 score:", f1_score(Y_test, Y_predict))
      f1_scores.append(f1_score(y_val, Y_predict))
      # print("Recall score:", recall_score(Y_test, Y_predict))
      recall_scores.append(recall_score(y_val, Y_predict))


plt.title("Cum influenteaza nr de frunze asupra accuracy")
plt.plot(range(1, 20, 2), acc_scores, 'x')
# plt.plot(max_leaf_nodes_list, f1_scores, 'o')
# plt.plot(max_leaf_nodes_list, recall_scores, 'x')
plt.xlabel('Max depth')
plt.ylabel('Accuracy score')

Text(0, 0.5, 'Accuracy score')

png

alegem cei mai buni parametri

clf = DecisionTreeClassifier(max_depth=6)
clf.fit(x_train, y_train)
Y_predict = clf.predict(x_test)
print("Accuracy score:", accuracy_score(y_test, Y_predict))
print("F1 score:", f1_score(y_test, Y_predict))
print("Recall score:", recall_score(y_test, Y_predict))

Accuracy score: 1.0
F1 score: 1.0
Recall score: 1.0

Interpretarea rezultatelor

clf = DecisionTreeClassifier(max_depth=4)
clf.fit(X_train, Y_train)

DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',
                       max_depth=6, max_features=None, max_leaf_nodes=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, presort='deprecated',
                       random_state=None, splitter='best')

from sklearn.tree import DecisionTreeClassifier, plot_tree
plt.figure(figsize=(20, 20))
plot_tree(clf)

[Text(485.2173913043478, 1009.5428571428572, 'X[8] <= 3.5\ngini = 0.499\nsamples = 5443\nvalue = [2830, 2613]'),
 Text(242.6086956521739, 854.2285714285715, 'X[20] <= 3.5\ngini = 0.277\nsamples = 2214\nvalue = [368, 1846]'),
 Text(97.04347826086956, 698.9142857142858, 'X[19] <= 1.5\ngini = 0.215\nsamples = 399\nvalue = [350, 49]'),
 Text(48.52173913043478, 543.6, 'gini = 0.0\nsamples = 31\nvalue = [0, 31]'),
 Text(145.56521739130434, 543.6, 'X[7] <= 0.5\ngini = 0.093\nsamples = 368\nvalue = [350, 18]'),
 Text(97.04347826086956, 388.28571428571433, 'gini = 0.0\nsamples = 350\nvalue = [350, 0]'),
 Text(194.08695652173913, 388.28571428571433, 'gini = 0.0\nsamples = 18\nvalue = [0, 18]'),
 Text(388.17391304347825, 698.9142857142858, 'X[10] <= 2.0\ngini = 0.02\nsamples = 1815\nvalue = [18, 1797]'),
 Text(339.6521739130435, 543.6, 'X[12] <= 0.5\ngini = 0.008\nsamples = 1804\nvalue = [7, 1797]'),
 Text(291.1304347826087, 388.28571428571433, 'X[7] <= 0.5\ngini = 0.434\nsamples = 22\nvalue = [7, 15]'),
 Text(242.6086956521739, 232.97142857142865, 'gini = 0.0\nsamples = 15\nvalue = [0, 15]'),
 Text(339.6521739130435, 232.97142857142865, 'gini = 0.0\nsamples = 7\nvalue = [7, 0]'),
 Text(388.17391304347825, 388.28571428571433, 'gini = 0.0\nsamples = 1782\nvalue = [0, 1782]'),
 Text(436.695652173913, 543.6, 'gini = 0.0\nsamples = 11\nvalue = [11, 0]'),
 Text(727.8260869565217, 854.2285714285715, 'X[19] <= 1.5\ngini = 0.362\nsamples = 3229\nvalue = [2462, 767]'),
 Text(582.2608695652174, 698.9142857142858, 'X[10] <= 0.5\ngini = 0.211\nsamples = 485\nvalue = [58, 427]'),
 Text(533.7391304347826, 543.6, 'gini = 0.0\nsamples = 58\nvalue = [58, 0]'),
 Text(630.7826086956521, 543.6, 'gini = 0.0\nsamples = 427\nvalue = [0, 427]'),
 Text(873.391304347826, 698.9142857142858, 'X[7] <= 0.5\ngini = 0.217\nsamples = 2744\nvalue = [2404, 340]'),
 Text(727.8260869565217, 543.6, 'X[14] <= 1.5\ngini = 0.048\nsamples = 2320\nvalue = [2263, 57]'),
 Text(679.304347826087, 388.28571428571433, 'gini = 0.0\nsamples = 26\nvalue = [0, 26]'),
 Text(776.3478260869565, 388.28571428571433, 'X[17] <= 1.5\ngini = 0.027\nsamples = 2294\nvalue = [2263, 31]'),
 Text(727.8260869565217, 232.97142857142865, 'gini = 0.0\nsamples = 2042\nvalue = [2042, 0]'),
 Text(824.8695652173913, 232.97142857142865, 'X[19] <= 6.0\ngini = 0.216\nsamples = 252\nvalue = [221, 31]'),
 Text(776.3478260869565, 77.65714285714284, 'gini = 0.0\nsamples = 31\nvalue = [0, 31]'),
 Text(873.391304347826, 77.65714285714284, 'gini = 0.0\nsamples = 221\nvalue = [221, 0]'),
 Text(1018.9565217391304, 543.6, 'X[9] <= 0.5\ngini = 0.444\nsamples = 424\nvalue = [141, 283]'),
 Text(970.4347826086956, 388.28571428571433, 'X[21] <= 1.5\ngini = 0.346\nsamples = 364\nvalue = [81, 283]'),
 Text(921.9130434782609, 232.97142857142865, 'gini = 0.0\nsamples = 188\nvalue = [0, 188]'),
 Text(1018.9565217391304, 232.97142857142865, 'X[3] <= 0.5\ngini = 0.497\nsamples = 176\nvalue = [81, 95]'),
 Text(970.4347826086956, 77.65714285714284, 'gini = 0.146\nsamples = 88\nvalue = [81, 7]'),
 Text(1067.4782608695652, 77.65714285714284, 'gini = 0.0\nsamples = 88\nvalue = [0, 88]'),
 Text(1067.4782608695652, 388.28571428571433, 'gini = 0.0\nsamples = 60\nvalue = [60, 0]')]

png

sa ne uitam la niste exemple

test_data = X_test.copy()
test_data['y_true'] = Y_test['class']
test_data['y_pred'] = clf.predict(X_test)

test_data.head()

	cap-shape	cap-surface	cap-color	bruises	odor	gill-attachment	gill-spacing	gill-size	gill-color	stalk-shape	stalk-root	stalk-surface-above-ring	stalk-surface-below-ring	stalk-color-above-ring	stalk-color-below-ring	veil-type	veil-color	ring-number	ring-type	spore-print-color	population	habitat	y_true	y_pred
1971	2	0	4	0	5	1	1	0	3	1	3	2	0	7	7	0	2	1	0	3	3	1	0	0
6654	2	2	2	0	8	1	0	1	0	1	0	2	2	6	6	0	2	1	0	7	4	2	1	1
5606	5	3	4	0	2	1	0	1	0	1	0	1	2	7	6	0	2	1	0	7	4	2	1	1
3332	2	3	3	1	5	1	0	0	5	1	1	2	2	3	6	0	2	1	4	3	5	0	0	0
6988	2	2	2	0	7	1	0	1	0	1	0	2	2	6	6	0	2	1	0	7	4	2	1	1

image.png

test_data['TP'] = (test_data['y_true'] == test_data['y_pred']) & (test_data['y_true'] == 1)

test_data[test_data['TP']].head()

	cap-shape	cap-surface	cap-color	bruises	odor	gill-attachment	gill-spacing	gill-size	gill-color	stalk-shape	stalk-root	stalk-surface-above-ring	stalk-surface-below-ring	stalk-color-above-ring	stalk-color-below-ring	veil-type	veil-color	ring-number	ring-type	spore-print-color	population	habitat	y_true	y_pred	TP
6654	2	2	2	0	8	1	0	1	0	1	0	2	2	6	6	0	2	1	0	7	4	2	1	1	True
5606	5	3	4	0	2	1	0	1	0	1	0	1	2	7	6	0	2	1	0	7	4	2	1	1	True
6988	2	2	2	0	7	1	0	1	0	1	0	2	2	6	6	0	2	1	0	7	4	2	1	1	True
5761	5	3	4	0	8	1	0	1	0	1	0	1	2	7	6	0	2	1	0	7	4	2	1	1	True
5798	5	2	3	1	2	1	0	0	3	1	1	2	0	7	7	0	2	1	4	1	3	5	1	1	True

test_data[test_data['TP']].describe()

	cap-shape	cap-surface	cap-color	bruises	odor	gill-attachment	gill-spacing	gill-size	gill-color	stalk-shape	stalk-root	stalk-surface-above-ring	stalk-surface-below-ring	stalk-color-above-ring	stalk-color-below-ring	veil-type	veil-color	ring-number	ring-type	spore-print-color	population	habitat	y_true	y_pred
count	1302.000000	1302.000000	1302.000000	1302.000000	1302.000000	1302.000000	1302.000000	1302.000000	1302.000000	1302.000000	1302.000000	1302.000000	1302.000000	1302.000000	1302.000000	1302.0	1302.0	1302.000000	1302.000000	1302.000000	1302.00000	1302.000000	1302.0	1302.0
mean	3.447773	2.058372	4.404762	0.168203	3.951613	0.996160	0.026882	0.565284	2.920891	0.505376	0.710445	1.360215	1.402458	5.505376	5.506912	0.0	2.0	1.011521	1.559140	3.969278	4.02381	1.905530	1.0	1.0
std	1.439518	1.102566	2.640617	0.374190	2.571043	0.061874	0.161800	0.495910	3.303837	0.500163	0.809170	0.561416	0.588849	2.196962	2.193807	0.0	0.0	0.163614	1.565615	2.839110	0.59479	1.805573	0.0	0.0
min	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	2.0	0.000000	0.000000	1.000000	1.00000	0.000000	1.0	1.0
25%	2.000000	2.000000	2.000000	0.000000	2.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	1.000000	6.000000	6.000000	0.0	2.0	1.000000	0.000000	1.000000	4.00000	0.000000	1.0	1.0
50%	3.000000	2.000000	4.000000	0.000000	2.000000	1.000000	0.000000	1.000000	2.000000	1.000000	1.000000	1.000000	1.000000	6.000000	6.000000	0.0	2.0	1.000000	2.000000	3.000000	4.00000	1.000000	1.0	1.0
75%	5.000000	3.000000	5.000000	0.000000	7.000000	1.000000	0.000000	1.000000	7.000000	1.000000	1.000000	2.000000	2.000000	7.000000	7.000000	0.0	2.0	1.000000	2.000000	7.000000	4.00000	4.000000	1.0	1.0
max	5.000000	3.000000	9.000000	1.000000	8.000000	1.000000	1.000000	1.000000	11.000000	1.000000	3.000000	2.000000	3.000000	7.000000	8.000000	0.0	2.0	2.000000	4.000000	7.000000	5.00000	5.000000	1.0	1.0

# cum gasim pe TN?

# cum gasim pe FN?

# cum gasim pe FP?

Boundaries plot

import numpy as np

col1 = 8
col2 = 19
x_min, x_max = -1,  X_test.iloc[:,col1].max()+1
y_min, y_max = -1,  X_test.iloc[:,col2].max()+1
h = 0.1
# cream un grid de puncte cu 2 coordonate care se incadreaza in coordonatele de sus
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
# xx - matrice cu 2 dimensiuni (randuri, coloane), cu coordonatele lui x pentru toate punctele
x_grid = np.c_[xx.ravel(), yy.ravel()] # -> (randuri * coloane, 2)
x_new = np.zeros((x_grid.shape[0], 22))
x_new[:, col1] = x_grid[:,0]
x_new[:, col2] = x_grid[:,1]
Z = clf.predict(x_new).reshape(xx.shape)
plt.figure(figsize=(x_max,y_max))
plt.title('hotare de decizii pentru coloanele {} si  {}'.format(col1,col2))
plt.contourf(xx, yy, Z, alpha=0.4)
plt.xlabel(data_encoded.columns[col1]+ "col_nr:{}".format(col1))
plt.ylabel(data_encoded.columns[col2]+ "col_nr:{}".format(col2))
plt.scatter(X_test.iloc[:,col1], X_test.iloc[:,col2], c=Y_test['class'], alpha=0.8)

png

• https://numpy.org/doc/stable/reference/generated/numpy.meshgrid.html

• https://numpy.org/doc/stable/reference/generated/numpy.ravel.html

• https://numpy.org/doc/stable/reference/generated/numpy.c_.html

  *https://matplotlib.org/api/_as_gen/matplotlib.pyplot.contourf.html